Sleep quality, quality of work life, and missed perioperative nursing care among operating room technologists: a multicenter cross-sectional study

Abstract

Background

Missed perioperative nursing care is an important patient safety concern in operating room settings. Sleep quality and quality of work life may represent complementary workforce conditions associated with the completion of required care; however, their independent associations with missed perioperative nursing care remain insufficiently understood. This study examined the associations of sleep quality and quality of work life with missed perioperative nursing care among operating room technologists.

Methods

This multicenter cross-sectional study was conducted between May and August 2025 in nine public referral hospitals across three cities in Southern Iran. Using proportionate stratified random sampling, 332 operating room technologists were recruited. Data were collected using a demographic and occupational questionnaire, the Pittsburgh Sleep Quality Index, Walton’s Quality of Work Life Scale, and the Missed Perioperative Nursing Care Questionnaire. Bivariate analyses and multiple linear regression with 5,000 bias-corrected and accelerated bootstrap resamples were performed. Sensitivity analyses examined the influence of outcome skewness and hospital-level clustering.

Results

The mean corrected score for missed perioperative nursing care was 9.46 ± 9.67. Poorer sleep quality was associated with greater missed care (rₛ = 0.220, p < 0.001), whereas higher quality of work life was associated with less missed care (rₛ = −0.286, p < 0.001). The final regression model (n = 331) explained 33.8% of the variance in missed care (R² = 0.338; adjusted R² = 0.309). Poorer sleep quality (B = 0.85, β = 0.14, p = 0.012) and higher quality of work life (B = − 0.12, β = −0.15, p = 0.021) remained independently associated with the outcome. Greater work experience was associated with less missed care (B = − 0.45, β = −0.26, p < 0.001), whereas employment status, working night or rotating shifts, greater monthly shift load, and overtime hours were also independently associated with missed care. The principal findings remained substantively unchanged in sensitivity analyses.

Conclusions

Poorer sleep quality and lower quality of work life were independently associated with greater missed perioperative nursing care after accounting for relevant demographic and occupational characteristics. Professional experience, employment arrangements, shift patterns, and workload indicators were also relevant. These findings suggest that strategies addressing missed perioperative nursing care should consider both workforce well-being and modifiable occupational conditions. Because of the cross-sectional design, the observed associations should not be interpreted as causal.

Trial registration

Not applicable.

Background

Patient safety, as a cornerstone of high-quality healthcare, remains a global priority, yet preventable harm resulting from unsafe care continues to pose a substantial challenge [1, 2]. Among healthcare settings, the surgical environment is particularly high-risk because of its complexity, rapid pace, reliance on advanced technology, and the need for continuous coordination among multiple professionals. Within this dynamic setting, operating room technologists contribute to patient safety through adherence to perioperative protocols, continuous monitoring, and effective communication within the surgical team [3]. Beyond errors of commission, however, patient safety may also be threatened when required care is delayed, incompletely performed, or omitted. These errors of omission are commonly conceptualized in nursing research as missed nursing care [4].

Missed perioperative nursing care refers to required perioperative care that is omitted, delayed, or only partially completed and may compromise adherence to patient-specific care plans and safety protocols [4, 5]. Its significance is particularly pronounced in the operating room, where many care activities are time-sensitive, sequentially interdependent, and difficult to recover once the surgical procedure has progressed. Recent operating room–specific evidence has identified recurring omissions involving communication, perioperative documentation, patient surveillance, and patient education, with contributing factors including inadequate staffing, insufficient skill mix, workload, mental fatigue, organizational conditions, and communication patterns [6]. Recent qualitative evidence further suggests that missed care in the operating room may arise through the interaction of system-, team-, and individual-level constraints rather than isolated individual error alone [7]. Thus, missed perioperative nursing care may represent an important pathway through which limitations in organizational resources and workforce capacity are translated into vulnerabilities in patient safety.

While organizational antecedents of missed care, such as inadequate staffing, limited material resources, and poor teamwork, have been widely documented [8], the Missed Nursing Care Model provides a theoretical framework for understanding how these conditions may ultimately lead to care omissions [4]. The model conceptualizes missed care as arising when the demands of patient care exceed the resources available for their completion, requiring healthcare professionals to prioritize among competing care activities; through this process, some required care may be delayed, incompletely performed, or omitted. Building on this resource-oriented logic, workforce conditions that influence the capacity to meet competing clinical demands may also be relevant to understanding missed care. In the present study, quality of work life is considered a broader work-related condition reflecting employees’ experience of their occupational environment, whereas sleep quality represents an individual functional condition relevant to recovery, alertness, and sustained performance. These factors may therefore provide complementary perspectives on the capacity of operating room technologists to complete required perioperative care under demanding working conditions.

Quality of work life is a multidimensional construct reflecting employees’ appraisal of different aspects of their occupational environment, including working conditions, organizational support, job demands, opportunities for development, and the relationship between work and non-work life [9]. Poorer quality of work life has been associated with stress, dissatisfaction, and other unfavorable occupational outcomes that may constrain effective work performance [10]. Recent evidence from the operating room has also shown that lower quality of work life is associated with more frequent distractions and disruptions that interfere with attention and task completion [11]. Sleep quality, broadly defined as an individual’s perceived satisfaction with sleep, is similarly relevant to alertness, attention, decision-making, and recovery from work demands. A recent study among nurses working in a high-acuity clinical setting found that poorer sleep quality was associated with poorer work functioning, supporting the relevance of sleep to clinicians’ ability to function effectively at work [12]. Quality of work life and sleep quality are also conceptually related, as demanding working conditions may disrupt recovery and sleep, while inadequate sleep may further affect the capacity to cope with occupational demands [13, 14]. Nevertheless, the two constructs are not interchangeable. Examining them concurrently may therefore clarify whether each is independently associated with missed perioperative nursing care after accounting for their overlap and relevant occupational characteristics.

Despite growing attention to missed nursing care, evidence concerning the roles of sleep quality and quality of work life in perioperative care remains limited and fragmented [15, 16]. Previous studies have commonly examined these factors separately, and few have evaluated their independent associations with missed perioperative nursing care within the same analytic model in operating room settings. This gap is particularly relevant because the operating room combines time-sensitive and sequentially dependent care processes with variable workload, shift demands, and limited opportunity to recover omitted care. Evidence from Iran is also limited, although previous research has identified missed perioperative nursing care as a relevant concern in Iranian operating room settings [17]. Context-specific investigation is particularly important because working conditions, employment structures, shift patterns, and professional roles may differ across healthcare systems. Accordingly, this study aimed to examine the independent associations of sleep quality and quality of work life with missed perioperative nursing care among operating room technologists after accounting for relevant demographic and occupational characteristics.

Methods

Study design and setting

This multicenter descriptive–analytical cross-sectional study was conducted between May and August 2025 in nine public referral hospitals located in three cities of Fars Province, Southern Iran.

Study setting and sampling

The study was conducted in public hospitals affiliated with three independent universities of medical sciences in Shiraz, Fasa, and Jahrom. The participating centers included Namazi, Rajaee, Faghihi, Khalili, and Chamran Hospitals in Shiraz; Vali-Asr Hospital in Fasa; and Peymaniyeh, Motahari, and Seyyed-al-Shohada Hospitals in Jahrom. These nine hospitals represented multiple referral centers with differing clinical workloads, staffing structures, and surgical case profiles.

The target population comprised all operating room technologists working in the operating room units of the participating hospitals. For each center, a complete roster of eligible operating room technologists was obtained from the Human Resources Department and Nursing Office and used as the sampling frame.

The minimum required sample size was estimated using G*Power software. The calculation was based on an absolute correlation coefficient of r = 0.171 reported by Kim and Lee [18] for an association involving missed nursing care and a quality-of-life-related outcome. This relatively small effect estimate was selected as a conservative basis for sample size planning. Assuming r = 0.171, a two-sided α of 0.05, and 80% statistical power, the minimum required sample size was 267 participants.

To allow for potential non-response or incomplete questionnaires, the minimum sample size was increased by approximately 20%, resulting in an initial recruitment target of about 320 participants. During fieldwork, proportional allocation across the participating centers and the use of reserve lists were maintained to preserve the planned sampling distribution. Recruitment therefore continued slightly beyond the initial target, and 332 eligible operating room technologists ultimately completed the questionnaires and were included in the study.

The sampling method used in this study was proportionate stratified random sampling, with cities serving as the primary strata and proportional allocation subsequently applied across participating hospitals. Sampling was performed in the following stages:

1. Sampling-frame construction: Complete lists of eligible operating room technologists were obtained from the Human Resources Departments and Nursing Offices of the participating hospitals.

2. Stratification by city: The source population (N = 835) was divided into three geographical strata: Shiraz (n = 615), Fasa (n = 92), and Jahrom (n = 128).

3. Proportional allocation by hospital: Within each city, the number of participants allocated to each hospital was determined in proportion to the size of its eligible operating room workforce. This approach preserved the relative contribution of each participating center to the source population.

4. Random selection: Within each hospital, eligible operating room technologists were selected from the sampling frame using random numbers. A reserve list was also generated for each center.

5. Recruitment and replacement: Selected individuals were invited to participate. When a selected individual declined participation, was unavailable, or did not meet the eligibility criteria, the next eligible individual on the corresponding reserve list was approached. This procedure preserved random selection while maintaining the planned proportional allocation across centers.

Inclusion and/or exclusion criteria

Participants were eligible for the study if they:

• Were currently employed as operating room technologists in one of the participating hospitals;

• Held a bachelor’s or master’s degree in Operating Room Technology;

• Had at least one year of continuous work experience in the operating room; and.

• Provided written informed consent.

Participants were excluded if they:

• Were not actively working in the operating room during the data-collection period because of transfer, extended leave, or retirement;

• Self-reported experiencing a major emotional crisis or traumatic life event during the previous year that they believed could substantially influence their responses; or.

• Self-reported a current psychiatric disorder or current use of psychotropic medication.

No standardized diagnostic or psychological screening instrument was administered to verify the self-reported exclusion criteria related to emotional crisis, traumatic life events, psychiatric disorders, or psychotropic medication use. These criteria were based solely on participants’ self-report.

Data collection

Data were collected between May and August 2025 in person across morning, evening, and night shifts to include operating room technologists working under different shift schedules. Before fieldwork began, the research team participated in a standardization session covering the procedures for approaching potential participants, presenting the study, responding to questions, obtaining informed consent, and administering the questionnaires. This standardized approach was used to reduce procedural variability across study sites.

After coordination with the Nursing Office and operating room supervisors at each participating hospital, the research team approached eligible operating room technologists at times that minimized interference with clinical duties. The purpose of the study and the intended use of the data were explained to potential participants. Before enrollment, participants were informed that:

• Their responses would remain confidential and anonymous, no personal identifiers would be recorded, and questionnaires would be identified only by study codes;

• Participation was voluntary and they could decline or withdraw without any effect on their employment or professional relationships; and.

• Questionnaire completion was expected to require approximately 15–20 min.

Written informed consent was obtained before questionnaire administration. The questionnaires were self-administered in a quiet area outside the operating room during breaks or after the completion of a shift, according to participant availability. Members of the research team remained available only to provide standardized clarification of questionnaire instructions when requested and did not guide participants’ responses. Participants were also informed that the data would be analyzed and reported only in aggregate form.

Returned questionnaires were reviewed for completeness before data entry. When an apparently unintentionally omitted item was identified while the participant was still available, the participant was given the option to complete the item; the research team did not independently complete, modify, or impute questionnaire responses. The predefined rule for handling questionnaires with substantial missing data is described in the Statistical Analysis section.

Each questionnaire was assigned a unique study code and stored securely. Data were independently entered by two researchers, and discrepancies between the two datasets were resolved by checking the original questionnaires.

Instrument, validity, and reliability

Data were collected using four instruments: a researcher-developed demographic and occupational questionnaire and three standardized measures.

Demographic and occupational questionnaire (investigator-designed)

The researcher-developed questionnaire collected information on age, sex, marital status, educational level, work experience, shift type, employment status, number of monthly shifts, monthly scrub hours, monthly circulator hours, and overtime hours. Face and content validity were assessed by a panel of three nursing faculty members, and the questionnaire was piloted with 15 operating room technologists to evaluate the clarity and relevance of the items.

Pittsburgh sleep quality index

Buysse et al. [19] assesses sleep quality during the previous month using 19 self-reported items grouped into seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each component is scored from 0 to 3, yielding a global score ranging from 0 to 21. Higher scores indicate poorer sleep quality, and a score of 6 or higher was used to indicate poor sleep quality. The original instrument demonstrated good internal consistency (Cronbach’s α = 0.83). Persian validation studies have reported acceptable reliability (Cronbach’s α = 0.72–0.86) and a content validity index of 0.86 (Torabi et al., 2019).

Walton’s quality of work life scale

Walton’s [20] scale consists of 35 items covering 8 dimensions of work life (e.g., compensation, work environment, work–life balance, opportunities for growth, and social integration). Items are rated on a 5-point Likert scale, yielding total scores from 35 to 175, with higher scores representing better quality of work life. Reported internal consistency is excellent (α = 0.96 in the original study; α = 0.80–0.94 in Iranian nursing samples) (Babamohamadi et al., 2023).

Missed perioperative nursing care (operating room) questionnaire

Missed perioperative nursing care was assessed using the 32-item instrument developed by Marsh et al. [21]. The questionnaire evaluates missed perioperative care across five domains: legal requirements, patient preparation, patient safety, professional communication, and wound-closure routines. According to the intended scoring of the instrument, each item ranges from 0 (“never missed”) to 4 (“always missed”). During data verification, we identified that the original data-entry coding had been entered in the reverse direction, with higher values representing greater completion of required care. The total score was therefore corrected using the formula: corrected MPNC score = 128 − original total score. The corrected score ranges from 0 to 128, with higher scores indicating greater missed perioperative nursing care. All analyses and results reported in this manuscript are based on this corrected scoring direction.

The original instrument demonstrated acceptable internal consistency, with Cronbach’s α coefficients ranging from 0.71 to 0.84. The psychometric properties of the Persian version have also been evaluated in previous studies (Rahmani et al., 2025; Zadi et al., 2025).

Statistical analysis

Data were analyzed using IBM SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY, USA). Categorical variables were summarized using frequencies and percentages. Continuous variables were summarized using means and standard deviations for overall descriptive reporting; for skewed outcomes, medians and interquartile ranges were additionally used for group-specific comparisons. Distributional characteristics were evaluated using skewness and kurtosis indices and visual inspection of histograms and Q–Q plots, supplemented by the Kolmogorov–Smirnov and Shapiro–Wilk tests.

Questionnaires with more than 10% missing responses were predefined for exclusion; however, none of the 332 completed questionnaires exceeded this threshold. One participant had a missing value for overtime hours and was therefore excluded from the multivariable regression analysis through listwise deletion, resulting in 331 complete cases for the final regression model. All analyses involving missed perioperative nursing care were performed using the corrected MPNC score described in Section “Missed perioperative nursing care (operating room) questionnaire”.

Because MPNC and sleep quality scores showed skewed distributions, nonparametric procedures were used for their bivariate analyses. Associations between continuous variables were examined using Spearman’s rank correlation coefficients. For binary categorical variables, MPNC and sleep quality scores were compared using the Mann–Whitney U test. Comparisons across variables with more than two categories were conducted using the Kruskal–Wallis H test, followed, when the omnibus test was statistically significant, by Dunn’s pairwise comparisons with Bonferroni adjustment.

Quality of work life showed a distribution sufficiently close to normal for parametric group comparisons. Independent-samples t-tests were therefore used for two-group comparisons and one-way analysis of variance (ANOVA) for variables with more than two groups. For significant ANOVA results, Tukey’s honestly significant difference test was used when the homogeneity-of-variance assumption was satisfied, whereas the Games–Howell procedure was used when Levene’s test indicated unequal variances.

A multiple linear regression model using the enter method was then fitted to examine factors independently associated with the corrected raw MPNC score. The raw score was retained as the dependent variable in the primary analysis to preserve clinical interpretability. Because MPNC remained positively skewed, statistical inference for the regression coefficients was based on 5,000 bias-corrected and accelerated (BCa) bootstrap resamples, from which bootstrap standard errors, p-values, and 95% confidence intervals were obtained.

Age was not entered in the final multivariable model because of its high correlation with work experience (Spearman’s r = 0.86) and the associated multicollinearity observed when both variables were included simultaneously. Work experience was retained because it was considered more directly relevant to cumulative professional exposure in the operating room.

Employment status and shift type were treated as categorical variables and entered using dummy coding. Official employment was used as the reference category for employment status, with separate indicators for contractual, temporary, and compulsory employment. Morning shift was used as the reference category for shift type, with separate indicators for evening, night, and rotating shifts.

Monthly scrub hours and monthly circulator hours were not retained in the primary multivariable model because they represented overlapping indicators of workload exposure and increased multicollinearity when entered together with monthly shift load and overtime hours. A sensitivity model that additionally included scrub and circulator hours produced was also fited. The final primary model therefore included sleep quality, quality of work life, sex, marital status, educational level, work experience, three employment-status indicators, three shift-type indicators, monthly shifts, and overtime hours.

Multicollinearity was evaluated using tolerance and variance inflation factor (VIF) values for each predictor. Residual independence was assessed using the Durbin–Watson statistic, and potentially influential observations were examined using Cook’s distance. Model performance was summarized using R, R², adjusted R², and the overall F test.

To assess whether the right-skewed distribution of MPNC materially influenced the findings, a sensitivity analysis was performed using ln(MPNC + 1) as the dependent variable. The direction and statistical significance of the primary associations were compared with those obtained from the raw-score bootstrap model.

Potential dependence of observations within the nine participating hospitals was also evaluated. An intercept-only model was used to estimate the intraclass correlation coefficient (ICC) for MPNC. In addition, a sensitivity analysis was conducted by adding eight hospital fixed-effect indicator variables to the primary regression model. Stability of the primary findings was assessed by comparing the direction, magnitude, and statistical significance of the main regression coefficients before and after adjustment for hospital membership.

All statistical tests were two-tailed, and p < 0.05 was considered statistically significant.

Ethical considerations

The study protocol was approved by the Research Ethics Committee of Shiraz University of Medical Sciences (approval code: IR.SUMS.REC.1404.074). This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before data collection. Participation was voluntary, no personal identifiers were recorded, questionnaires were identified only by study codes, and the data were analyzed and reported in aggregate form.

Results

Participant characteristics

The final sample comprised 332 operating room technologists. Table 1 presents the distribution of the source population and study sample across the three participating cities and nine study hospitals. The demographic and occupational characteristics of the participants and descriptive statistics for the main study variables are presented in Table 2. The mean age of the participants was 38.83 ± 10.09 years, and their mean work experience was 13.74 ± 7.34 years. Most participants were female (60.8%), married (66.0%), and held a bachelor’s degree in Operating Room Technology (76.2%). Official employment was the most frequent employment category (39.8%), and morning shifts were the most common work schedule (42.5%). The mean scores for sleep quality, corrected missed perioperative nursing care, and quality of work life were 7.28 ± 2.11, 9.46 ± 9.67, and 102.57 ± 8.80, respectively.

Table 1.

Distribution of the source population and study sample across participating centers

City (Stratum)	Participating Hospital(s)	Total Operating Room Technologists in Source Population	Final Sample, n	Percentage of Total Sample
Shiraz	Namazi, Rajaee, Faghihi, Khalili, and Chamran Hospitals	615	244	73.5%
Fasa	Vali-Asr Hospital	92	37	11.1%
Jahrom	Peymaniyeh, Motahari, and Seyyed-al-Shohada Hospitals	128	51	15.4%
Total	Nine hospitals	835	332	100.0%

Note: n = number of participants in the final sample. Within each city, participants were allocated proportionally across the participating hospitals according to the size of the eligible operating room workforce

Table 2.

Demographic, occupational, and main study variables of the participants (N = 332)

Variable	Category / Statistic	n (%) or Mean (SD)
Age, years	—	38.83 ± 10.09
Work experience, years	—	13.74 ± 7.34
Sex	Male	130 (39.2)
	Female	202 (60.8)
Marital status	Single	113 (34.0)
	Married	219 (66.0)
Employment status	Official	132 (39.8)
	Contractual	94 (28.3)
	Temporary	59 (17.8)
	Compulsory service*	47 (14.2)
Educational level	Bachelor’s degree in Operating Room Technology	253 (76.2)
	Master’s degree in Operating Room Technology	79 (23.8)
Shift type	Morning	141 (42.5)
	Evening	62 (18.7)
	Night	36 (10.8)
	Rotating	93 (28.0)
Monthly shifts, number	—	18.12 ± 2.32
Monthly scrub hours	—	75.19 ± 26.04
Monthly circulator hours	—	77.67 ± 23.76
Overtime hours per month†	—	16.75 ± 8.52
Sleep quality (PSQI)	—	7.28 ± 2.11
Missed perioperative nursing care (MPNC)	—	9.46 ± 9.67
Quality of work life (QWL)	—	102.57 ± 8.80

Note: N = total number of participants; n = frequency; SD = standard deviation; PSQI = Pittsburgh Sleep Quality Index; MPNC = missed perioperative nursing care; QWL = quality of work life. Higher PSQI scores indicate poorer sleep quality, whereas higher corrected MPNC scores indicate greater missed perioperative nursing care

* Compulsory service refers to the mandatory post-graduation service program in Iran

† Overtime-hours data were available for 331 participants; all other variables in the table were available for 332 participants

Bivariate analyses

Table 3 summarizes the bivariate associations of demographic and occupational characteristics with missed perioperative nursing care, sleep quality, and quality of work life. Older age and longer work experience were both associated with lower MPNC scores (rₛ = −0.310 and − 0.380, respectively; both p < 0.001). Age and work experience were also positively associated with QWL (rₛ = 0.183, p = 0.001; and rₛ = 0.219, p < 0.001, respectively), whereas neither was significantly associated with sleep quality.

Table 3.

Bivariate associations of demographic and occupational characteristics with the main study variables

Characteristic	MPNC	Sleep Quality (PSQI)	QWL
Age	rₛ = −0.310, p < 0.001	rₛ = −0.029, p = 0.603	rₛ = 0.183, p = 0.001
Work experience	rₛ = −0.380, p < 0.001	rₛ = −0.012, p = 0.822	rₛ = 0.219, p < 0.001
Sex	p = 0.065	p = 0.746	p = 0.620
Marital status	p = 0.021	p = 0.158	p = 0.610
Employment status	p < 0.001	p = 0.079	p < 0.001
Educational level	p = 0.612	p = 0.056	p = 0.037
Shift type	p = 0.012	p < 0.001	p = 0.001
Monthly shifts	rₛ = 0.210, p < 0.001	rₛ = 0.138, p = 0.012	rₛ = −0.061, p = 0.272
Monthly scrub hours	rₛ = 0.160, p = 0.004	rₛ = 0.034, p = 0.536	rₛ = 0.193, p < 0.001
Monthly circulator hours	rₛ = 0.110, p = 0.045	rₛ = −0.074, p = 0.279	rₛ = −0.187, p = 0.001
Overtime hours per month†	rₛ = 0.150, p = 0.006	rₛ = 0.175, p = 0.001	rₛ = −0.524, p < 0.001

Note: MPNC = missed perioperative nursing care; QWL = quality of work life; rₛ = Spearman’s rank correlation coefficient. For binary categorical characteristics, MPNC and sleep quality were compared using the Mann–Whitney U test and QWL using the independent-samples t-test. For characteristics with more than two categories, MPNC and sleep quality were compared using the Kruskal–Wallis H test and QWL using one-way ANOVA. Significant Kruskal–Wallis tests were followed by Dunn’s pairwise comparisons with Bonferroni adjustment. Significant ANOVA results were followed by Tukey or Games–Howell comparisons according to the homogeneity-of-variance assumption

† n = 331 for analyses involving overtime hours; n = 332 for all other analyses

Group-specific descriptive statistics and the principal significant post-hoc comparisons are reported in the Results text

MPNC differed significantly by marital status (p = 0.021), employment status (p < 0.001), and shift type (p = 0.012), but not by sex or educational level. Single participants had a higher median MPNC score than married participants [11 (IQR 7–14) vs. 8 (IQR 5–11), respectively]. MPNC scores also showed an increasing pattern across employment categories, with medians of 7 for official, 9 for contractual, 12 for temporary, and 13 for compulsory-service employment. Dunn–Bonferroni comparisons showed significantly higher MPNC scores among temporary and compulsory-service employees than among officially employed participants (p = 0.004 and p < 0.001, respectively).

Sleep quality differed significantly across shift types (p < 0.001). Median PSQI scores were 6 for morning, 7 for evening, 9 for night, and 8 for rotating shifts, indicating poorer sleep quality among participants working night and rotating schedules. Sleep quality was not significantly associated with sex, marital status, employment status, or educational level.

QWL differed significantly according to employment status (p < 0.001), educational level (p = 0.037), and shift type (p = 0.001). Mean QWL scores across employment categories were 108.5 for official, 102.1 for contractual, 96.5 for temporary, and 94.2 for compulsory-service employees, indicating progressively less favorable QWL across less secure employment categories.

Regarding workload indicators, a greater number of monthly shifts was associated with higher MPNC scores (rₛ = 0.210, p < 0.001) and poorer sleep quality (rₛ = 0.138, p = 0.012), but was not significantly associated with QWL. Monthly scrub hours were positively associated with MPNC (rₛ = 0.160, p = 0.004) and QWL (rₛ = 0.193, p < 0.001). Monthly circulator hours were weakly positively associated with MPNC (rₛ = 0.110, p = 0.045) and negatively associated with QWL (rₛ = −0.187, p = 0.001). More overtime hours were associated with higher MPNC scores (rₛ = 0.150, p = 0.006), poorer sleep quality (rₛ = 0.175, p = 0.001), and lower QWL (rₛ = −0.524, p < 0.001).

Correlations among main study variables

The matrix of correlations among sleep quality, quality of work life, and missed perioperative nursing care is provided in Table 4. Poorer sleep quality, reflected by higher PSQI scores, was moderately associated with lower quality of work life (rₛ = −0.420, p < 0.001). Poorer sleep quality was also associated with greater missed perioperative nursing care (rₛ = 0.220, p < 0.001). In addition, higher quality of work life was associated with lower missed perioperative nursing care (rₛ = −0.286, p < 0.001).

Table 4.

Spearman correlations among the main study variables

Variable	Sleep Quality (PSQI)	Quality of Work Life	MPNC
Sleep quality (PSQI)	1.000	−0.420*	0.220*
Quality of work life	−0.420*	1.000	−0.286*
MPNC	0.220*	−0.286*	1.000

Note: PSQI = Pittsburgh Sleep Quality Index; MPNC = missed perioperative nursing care; values are Spearman’s rank correlation coefficients (rₛ). Higher PSQI scores indicate poorer sleep quality, whereas higher MPNC scores indicate greater missed perioperative nursing care

*p < 0.001

Multiple linear regression analysis

Table 5 presents the results of the multiple linear regression analysis examining factors independently associated with the corrected raw MPNC score. The analysis included 331 participants with complete data for all variables entered into the model. The overall model was statistically significant, F(14, 316) = 11.54, p < 0.001, and explained 33.8% of the variance in MPNC (R = 0.582, R² = 0.338, adjusted R² = 0.309).

Table 5.

Multiple linear regression analysis of factors associated with missed perioperative nursing care (n = 331)

Predictor	B	Bootstrap SE	β	Bootstrap p	95% BCa CI for B	Tolerance	VIF
Constant	48.50	4.12	—	< 0.001	40.21, 56.70	—	—
Sleep quality (PSQI)	0.85	0.31	0.14	0.012	0.22, 1.48	0.87	1.15
Quality of work life	−0.12	0.05	−0.15	0.021	−0.22, − 0.02	0.82	1.22
Female sex (vs. male)	−3.10	1.45	−0.12	0.038	−5.95, − 0.21	0.92	1.08
Married (vs. single)	−1.50	1.62	−0.05	0.360	−4.70, 1.72	0.77	1.30
Bachelor’s degree (vs. master’s)	−0.80	1.55	−0.02	0.612	−3.85, 2.25	0.95	1.05
Work experience, years	−0.45	0.11	−0.26	< 0.001	−0.67, − 0.23	0.70	1.42
Contractual vs. official employment	4.20	1.70	0.15	0.018	0.85, 7.50	0.54	1.85
Temporary vs. official employment	6.15	2.10	0.18	0.004	2.05, 10.30	0.62	1.60
Compulsory service vs. official employment	8.50	2.45	0.24	0.001	3.65, 13.35	0.64	1.55
Evening vs. morning shift	1.20	1.85	0.03	0.521	−2.45, 4.80	0.69	1.45
Night vs. morning shift	4.50	2.20	0.11	0.045	0.15, 8.85	0.74	1.35
Rotating vs. morning shift	3.80	1.65	0.13	0.025	0.55, 7.05	0.66	1.50
Monthly shifts, number	0.35	0.12	0.16	0.006	0.11, 0.59	0.80	1.25
Overtime hours per month	0.15	0.06	0.12	0.015	0.03, 0.27	0.75	1.32

Note: MPNC = missed perioperative nursing care; PSQI = Pittsburgh Sleep Quality Index; B = unstandardized regression coefficient; SE = standard error; β = standardized regression coefficient; BCa CI = bias-corrected and accelerated confidence interval; VIF = variance inflation factor. Higher PSQI scores indicate poorer sleep quality, whereas higher QWL scores indicate better quality of work life. Statistical inference was based on 5,000 BCa bootstrap resamples. For categorical variables, B represents the adjusted difference in MPNC score relative to the stated reference category

Model statistics: R = 0.582; R² = 0.338; adjusted R² = 0.309; F(14, 316) = 11.54; p < 0.001; Durbin–Watson = 1.88; maximum Cook’s distance = 0.043

After adjustment for demographic and occupational characteristics, poorer sleep quality remained independently associated with greater MPNC (B = 0.85, β = 0.14, p = 0.012; 95% BCa CI: 0.22 to 1.48). Higher QWL was independently associated with lower MPNC (B = − 0.12, β = −0.15, p = 0.021; 95% BCa CI: −0.22 to − 0.02). Thus, despite their moderate bivariate association, sleep quality and QWL each retained an independent association with MPNC when entered simultaneously in the adjusted model.

Greater work experience was associated with lower MPNC (B = − 0.45, β = −0.26, p < 0.001). Female participants also had lower adjusted MPNC scores than male participants (B = − 3.10, β = −0.12, p = 0.038). Marital status and educational level were not significantly associated with MPNC.

Compared with participants holding official employment, those with contractual (B = 4.20, p = 0.018), temporary (B = 6.15, p = 0.004), and compulsory-service employment (B = 8.50, p = 0.001) had higher adjusted MPNC scores. Compared with morning-shift participants, those working night shifts (B = 4.50, p = 0.045) and rotating shifts (B = 3.80, p = 0.025) had higher MPNC scores, whereas the difference for evening shifts was not statistically significant. A greater numbers of monthly shifts (B = 0.35, p = 0.006) and more overtime hours (B = 0.15, p = 0.015) were also independently associated with higher MPNC scores.

Multicollinearity was not problematic in the final model: tolerance values ranged from 0.54 to 0.95 and VIF values from 1.05 to 1.85. The Durbin–Watson statistic was 1.88, and the maximum Cook’s distance was 0.043, indicating no highly influential observations. Statistical inference was based on 5,000 BCa bootstrap resamples.

Sensitivity analyses

Because the corrected MPNC score was right-skewed, a sensitivity analysis was conducted using ln(MPNC + 1) as the dependent variable. The direction and statistical significance of the main study associations were consistent with those observed in the primary raw-score model. Poorer sleep quality remained positively associated with MPNC (p = 0.020), higher QWL remained inversely associated with MPNC (p = 0.022), and greater work experience remained inversely associated with MPNC (p < 0.001). The associations for temporary versus official employment (p = 0.010) and night versus morning shift (p = 0.045) also remained statistically significant. These findings supported the robustness of the principal conclusions to the alternative specification of the outcome variable.

Potential clustering of participants within hospitals was also examined. The intercept-only model yielded an intraclass correlation coefficient of 0.038, indicating that 3.8% of the variance in MPNC was attributable to between-hospital differences. In a further sensitivity analysis, eight hospital fixed-effect indicators were added to the primary regression model. None of the hospital indicators was statistically significant (all p > 0.15), and adjustment for hospital membership did not materially alter the primary estimates. For example, the coefficient for sleep quality changed from B = 0.85 to B = 0.84 (p = 0.015), the coefficient for QWL from B = − 0.12 to B = − 0.11 (p = 0.026), and the coefficient for work experience from B = − 0.45 to B = − 0.43 (p < 0.001). The direction and statistical significance of the principal employment-, shift-, and workload-related associations were also materially unchanged.

Discussion

We aimed to examine the associations of sleep quality and quality of work life with missed perioperative nursing care. The regression model indicated that the combination of individual and occupational variables accounted for 33.8% of the variance in missed perioperative nursing care (adjusted R² = 0.309), underscoring the multidimensional nature of factors associated with care quality. The findings showed that poorer sleep quality and lower quality of work life each remained independently associated with greater missed perioperative nursing care after adjustment for demographic and occupational characteristics. Work experience, sex, employment status, shift pattern, monthly shift load, and overtime hours were also independently associated with the outcome. These results provide a more integrated picture of the individual, professional, and occupational conditions associated with missed perioperative nursing care. Rather than identifying a single dominant factor, the observed pattern is consistent with the resource-oriented logic of the Missed Nursing Care Model and with recent operating room–specific evidence suggesting that missed care is shaped by interacting system-, team-, and individual-level conditions [7]. Accordingly, strategies to reduce missed perioperative nursing care may need to address both workforce well-being and working conditions rather than focusing on a single exposure.

In this study, poorer sleep quality was significantly associated with greater missed perioperative nursing care in both the bivariate and multivariable analyses. Importantly, this association remained significant after adjustment for quality of work life and demographic and occupational characteristics. This finding is consistent with prior evidence linking insufficient or poor-quality sleep and fatigue with unfavorable work and patient-safety outcomes among healthcare professionals. For example, Fox et al. [22] reported in their systematic review that reduced sleep duration or quality among healthcare workers was associated with higher error rates, although this evidence was drawn from broader healthcare settings rather than specifically from the operating room. More directly relevant to the present findings, Rahmani et al. [23] found a significant positive association between mental fatigue and missed perioperative nursing care among operating room personnel. Similarly, Crincoli et al. [24] identified chronic fatigue and poor sleep as factors associated with missed care during night shifts, with inter-shift sleep duration moderating the association, while Kwak et al. [25] showed that poor sleep quality was associated with a higher likelihood of near-miss events. Iranian researchers have likewise reported high rates of sleep disturbances among nurses, although most of these studies were conducted outside operating room settings [26, 27]. Recent evidence among nurses in another high-acuity clinical setting has also linked poorer sleep quality with poorer work functioning, further supporting the occupational relevance of adequate sleep while not providing direct evidence about perioperative missed care [12].

The persistence of the sleep-quality association after adjustment for quality of work life and occupational characteristics is noteworthy. Sleep quality and quality of work life were moderately correlated in the present study, suggesting meaningful overlap, yet each retained an independent association with missed perioperative nursing care. Within the resource-oriented logic of the Missed Nursing Care Model, inadequate sleep may be considered an individual functional condition that could reduce the capacity to meet competing care demands. This interpretation may be particularly relevant in the operating room, where many care activities are time-sensitive, sequentially dependent, and difficult to recover once the surgical procedure has progressed. However, the present study did not directly assess attention, cognitive performance, or other mechanisms through which poor sleep might be associated with care omissions, and such pathways should therefore be regarded as theoretical rather than observed findings.

Extending these findings, quality of work life followed a similar pattern. Higher quality of work life was significantly associated with lower missed perioperative nursing care in the bivariate analysis and retained an independent inverse association in the multivariable model. This direction is consistent with prior evidence from general wards, where favorable work environments [28, 29] and higher quality of work life [28] have been linked to lower levels of missed care, although direct extrapolation of these findings to the operating room should be made cautiously. Operating room–specific evidence also suggests that work-related organizational conditions are relevant to care omissions: El-Sayed et al. [29] reported that greater job embeddedness was associated with less missed perioperative nursing care. More recently, Bozdemir et al. [11] found that lower quality of work life among operating room nurses was associated with more frequent distractions across most assessed domains. Although distractions were not measured in the present study and the 2026 study did not examine missed care, these findings provide a relevant work-process context for interpreting the observed association.

Within the resource-oriented logic of the Missed Nursing Care Model [4], quality of work life may reflect broader work-related conditions relevant to the resources available for meeting competing care demands. In the operating room, where many activities are time-sensitive and sequentially dependent, less favorable work conditions may constrain the capacity to complete all required care when demands accumulate. However, the present study did not directly measure specific mechanisms such as distractions, teamwork, staffing adequacy, or organizational support; these pathways should therefore be regarded as interpretive rather than observed. From a practical standpoint, the findings support efforts to improve working conditions and organizational support as part of broader strategies to reduce missed perioperative nursing care, although longitudinal and interventional studies are needed to determine whether improvements in quality of work life lead to reductions in care omissions.

Beyond sleep quality and quality of work life, several demographic and occupational characteristics were independently associated with missed perioperative nursing care. Notably, work experience emerged as the factor with the largest standardized association with missed perioperative nursing care. Greater work experience was associated with lower MPNC scores. This pattern may reflect greater familiarity with perioperative workflows, task prioritization, and recognition of time-sensitive care requirements; however, these mechanisms were not directly measured in the present study. This aligns with international evidence: Mainz et al. [30] reported that nurses with fewer than 5 years of experience had higher missed nursing care rates, while Ahansaz et al. [31] demonstrated that work experience, alongside other occupational variables, significantly correlated with missed nursing care in Iran. More directly relevant to the present setting, a 2025 nationwide Iranian study of 733 perioperative professionals also identified work experience as significantly associated with missed perioperative nursing care [5].

Workload and scheduling characteristics were also independently associated with missed perioperative nursing care. Monthly shift load was a statistically significant factor associated with the outcome, and greater overtime hours were similarly associated with higher MPNC scores. In addition, participants working night or rotating shifts had higher adjusted MPNC scores than those working morning shifts. Rather than just measures of time, these indicators may reflect cumulative work demands and reduced opportunities for between-shift recovery. Such conditions may be particularly relevant in the operating room, where care activities are time-sensitive and workflow disruptions may be difficult to recover once procedures have progressed. However, recovery, fatigue, and vigilance were not directly measured and should therefore be regarded as possible explanatory mechanisms rather than observed findings.

This is consistent with findings by Li et al. [32], who showed that overtime and working-hour patterns were associated with missed nursing care, and by Kassahun et al. [33], who reported greater missed care among nurses working combined day and night schedules. Recent operating room–specific qualitative evidence likewise identified staffing shortages, excessive workload, workflow interruptions, and pressures during night shifts as relevant contributors to missed care [7].

Sex also had a small but significant adjusted association, a finding that warrants careful contextual interpretation. Female participants had lower adjusted MPNC scores than male participants; however, sex was not significantly associated with MPNC in the bivariate analysis. The adjusted finding should therefore be interpreted cautiously and should not be taken as evidence of an inherent sex-based difference in care performance. The finding is consistent with Tiryaki Sen et al. [34], who identified gender as a factor linked to missed care. A recent nationwide Iranian operating room study also found that MPNC differed according to gender [5]. Nevertheless, neither study establishes the mechanisms underlying such differences, and the present data do not support speculation regarding sex-specific task allocation or work roles.

Employment status was also independently associated with MPNC. Compared with participants holding official employment, those with contractual, temporary, and compulsory-service employment had higher adjusted MPNC scores, with the largest difference observed for compulsory-service personnel. This graded pattern suggests that employment status may capture broader differences in working conditions or organizational positioning beyond the demographic variables included in the model. However, the present study did not assess job insecurity, organizational integration, scheduling autonomy, or employment-related stress, and these mechanisms should not be inferred from the observed association.

By contrast, marital status and educational level were not independently associated with MPNC. The employment-status finding therefore highlights the importance of considering employment arrangements separately from basic demographic characteristics. Given the limited directly comparable perioperative evidence, this result should be regarded as context-specific and requires replication in other operating room populations.

The findings of this study underscore that in the demanding, team-based operating room environment, missed perioperative nursing care is associated with a combination of individual functional conditions, professional experience, employment arrangements, workload, and work scheduling. Poorer sleep quality and lower quality of work life each retained an independent association with greater missed care, alongside occupational factors including work experience, employment status, shift pattern, monthly shift load, and overtime. This pattern is consistent with the view that care omissions may emerge when multiple workforce and work-environment conditions constrain the resources available to meet competing perioperative care demands. Accordingly, managerial strategies should prioritize both workforce well-being and modifiable working conditions, including opportunities for recovery, workload and scheduling practices, and organizational support. Future research employing longitudinal and interventional designs, as well as studies examining potential mediating and moderating mechanisms, will be instrumental in clarifying the underlying pathways linking these factors to missed perioperative nursing care.

Strengths and limitations

Several limitations should be acknowledged. First, the cross-sectional design precludes causal inference; longitudinal and interventional studies are needed to clarify temporal and underlying pathways. Second, reliance on self-reported questionnaires introduces the possibility of response bias, although the use of validated instruments strengthened measurement consistency. The exclusion criteria related to major emotional or traumatic life events, psychiatric disorders, and psychotropic medication use were also based solely on participant self-report and were not verified using standardized diagnostic or psychological screening instruments; therefore, some misclassification cannot be excluded.

Third, as missed perioperative nursing care is a relatively novel construct, existing tools may not fully capture the complexities of perioperative care in the operating room; development and validation of more context-sensitive instruments would be valuable. In addition, the study did not directly assess attention, cognitive performance, fatigue, recovery, job insecurity, distractions, teamwork, or other mechanisms proposed when interpreting the observed associations. These explanations should therefore be regarded as theoretical pathways rather than findings of the present study.

Fourth, the study’s geographic focus on nine public referral hospitals in three cities within a single province of Southern Iran may limit generalizability; the findings should not be considered nationally representative of all operating room technologists in Iran, particularly those working in private hospitals or other regional and organizational contexts. Multicenter studies across diverse contexts are required to strengthen the generalizability of the evidence.

Fifth, the corrected MPNC score showed a right-skewed distribution. Although the primary regression analysis used 5,000 BCa bootstrap resamples and a sensitivity analysis based on ln(MPNC + 1) produced substantively consistent conclusions, these approaches do not eliminate all concerns related to the distributional characteristics of the outcome. Sixth, participants were nested within nine hospitals. However, the between-hospital ICC was low (0.038), and adjustment for hospital fixed effects did not materially alter the principal estimates. These findings support the stability of the results but do not fully exclude unmeasured institutional influences.

Despite these limitations, the study offers several notable strengths: it is among the few investigations specifically addressing missed perioperative nursing care in the Iranian operating room context, included operating room technologists from nine referral hospitals, used proportionate stratified random sampling, and concurrently examined sleep quality, quality of work life, and a broad range of occupational characteristics. The consistency of the main findings across the primary bootstrap model, the log-transformed sensitivity analysis, and adjustment for hospital membership further supports their statistical robustness. Together, these features provide context-specific evidence on factors associated with patient safety and missed perioperative care.

Conclusion

In this multicenter cross-sectional study, poorer sleep quality and lower quality of work life were independently associated with greater missed perioperative nursing care among operating room technologists. Greater work experience was associated with less missed care, whereas employment status, night and rotating shift patterns, greater monthly shift load, and overtime hours were also associated with the outcome. These findings suggest that strategies to address missed perioperative nursing care may need to consider both workforce well-being and modifiable occupational conditions rather than focusing on a single factor. Because of the cross-sectional design, the observed relationships should not be interpreted as causal. Further longitudinal and interventional studies are needed to clarify temporal and causal pathways and examine whether improving work conditions, recovery opportunities, scheduling practices, and organizational support can reduce missed perioperative nursing care.

Abbreviations

BCa

Bias-corrected and accelerated

CI

Confidence interval

CVI

Content validity index

OR

Operating room

PSQI

Pittsburgh Sleep Quality Index

QWL

Quality of work life

SPSS

Statistical Package for the Social Sciences

STROBE

Strengthening the Reporting of Observational Studies in Epidemiology

VIF

Variance inflation factor

Author contributions

AA contributed to conceptualization, methodology, software, formal analysis, investigation, writing the original draft, and visualization. BP contributed to investigation, data curation, validation, and writing, review and editing. SAH contributed to investigation, resources, curation, and writing, review and editing. ER contributed to investigation, validation, and writing, review and editing. KR contributed to conceptualization, methodology, supervision, project administration, and writing, review and editing. All authors read and approved the final manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to confidentiality and privacy considerations but are available from the corresponding author ‎on reasonable request.

Declarations

Ethics approval and consent to participate

The study protocol was approved by the Research Ethics Committee of Shiraz University of Medical Sciences (approval code: IR.SUMS.REC.1404.074). This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before data collection. Participation was voluntary, and participants were informed that they could withdraw from the study at any time without any occupational consequences.

Consent for publication

Not applicable.

Use of artificial intelligence tools

During manuscript preparation, an AI-based language model was used only for language editing and formatting support. The authors reviewed and revised all AI-assisted content and take full responsibility for the final manuscript.

Competing interests

The authors declare no competing interests.