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. 2026 Feb 12;12:23779608261415793. doi: 10.1177/23779608261415793

Developing and Validating the Caring Stress Management Scale for Nurses: A Comprehensive Protocol Study

Amir Hossein Goudarzian 1,2, Elham Navab 3, Alireza Nikbakht Nasrabadi 4, Hamid Sharif-Nia 5,6,✉
PMCID: PMC12901946  PMID: 41695610

Abstract

Introduction

To date, most research on nursing stress has addressed the full range of stressors in work environments. Caring stress can be viewed as a specific dimension of occupational stress, focusing on the psychological and physiological strain arising directly from patient care, rather than broader organizational or administrative factors.

Objectives

This study aims to explore the general dimensions of caring stress among nurses, focusing on the experiences of nurses working in healthcare facilities interacting with patients.

Methods

This study employs an exploratory sequential mixed-methods design. In the qualitative phase, interviews and conventional content analysis will gather insights on caring stress and management among nurses, which will inform the development of a preliminary scale. The quantitative phase involves psychometric testing of the scale, including face and content validity assessments, and exploratory and confirmatory factor analyses to confirm construct validity. Reliability will be measured using internal consistency (Cronbach's alpha) and test–retest methods. Responsiveness and interpretability will also be evaluated to ensure the scale's sensitivity to changes over time.

Discussion

By incorporating cultural and workplace factors specific to Iranian nurses, the scale offers a contextually relevant measure of caring-related stress and management strategies. Comprehensive validity and reliability assessments demonstrate the scale's potential as a robust instrument for guiding effective interventions and future research in occupational health among nurses.

Conclusion

The development and psychometric evaluation of the Caring Stress Management Scale provides a significant step forward for understanding the specific stressors faced by nurses in caring roles.

Keywords: caring stress management, psychometrics, nurse, protocol study, Iran, development

Background

Nursing is widely recognized as a highly stressful profession, associated with elevated rates of injury, job dissatisfaction, and turnover (Babapour et al., 2022; Singh et al., 2020). While most research examines broad workplace stressors, caring stress represents a specific form of occupational stress. This strain arises directly from patient care, stemming from continuous patient and family interactions, the emotional and ethical demands of critical or end-of-life care, and the cognitive burden of decision-making amid uncertainty (Costello et al., 2019; Kim & Kim, 2020).

The persistent nature of caring stress contributes significantly to severe negative outcomes. Chronic stress, often worsened by staffing shortages, inadequate resources, and weak managerial support, leads to burnout. Furthermore, it causes physical problems (e.g., hypertension, cardiovascular disease, sleep disorders) and psychological issues (e.g., depression, anxiety), ultimately reducing nurses’ quality of life and negatively impacting the quality of patient care (Peñacoba et al., 2021).

Stress management is one of the most effective ways to prevent or reduce these harmful effects in nursing (Hosseini Moghaddam et al., 2022). By helping nurses regulate stress and support their mental health, effective management strategies enhance professional well-being, happiness, and job satisfaction (Green & Kinchen, 2021). Caring stress management specifically involves intentional cognitive, emotional, and behavioral strategies used to handle the stressors arising from direct patient care (Fuller-Tyszkiewicz et al., 2020).

Review of Literature

One of the fundamental steps in objectively assessing the level of stress and efforts to improve stress management in nurses is the availability of a scale to measure it in this population. Various scales have been designed and used to assess stress, including stress perception (Cohen et al., 1994), traumatic stress (Bride et al., 2004), occupational stress (Chang et al., 2005), depression, anxiety, and stress (Parkitny & McAuley, 2010), academic stress (Stallman & Hurst, 2016), and parental stress (Berry & Jones, 1995). In 1981, Gray-Toft designed a Nursing Stress Scale that, unlike previous scales used for general stress assessment, is specialized for nurses (Gray-Toft & Anderson, 1981). However, there was no scale available for stress management in patient care. In 1997, Hansen designed a stress management skills scale, which was a significant breakthrough in this area, but this scale was not specific to nurses (Hansen & McNeal, 1997).

Although several scales assess stress in nursing and caregiving, none specifically measure how nurses manage stress during direct patient care. The Nursing Stress Scale (Gray-Toft & Anderson, 1981) identifies the frequency of occupational stressors but emphasizes exposure rather than coping behaviors in caring interactions. Likewise, stress-management scales (Hansen & McNeal, 1997) evaluate general coping skills or program-related factors but do not address the interpersonal, ethical, and context-specific processes central to nursing care.

A recently developed instrument for family caregivers of people with Alzheimer's disease (Sharif Nia et al., 2023) addresses caring stress management in informal caregivers, but its content is specific to the family caregiver role and cannot be generalized to professional nursing practice (Moustaka & Constantinidis, 2010; Vahedian-Azimi et al., 2019). Based on a scoping review and content analysis of existing instruments, we conclude that although related scales exist, no validated scale specifically measures caring stress management among professional nurses in the Iranian clinical context. This gap justifies the development and validation of the proposed Caring Stress Management Scale.

While international instruments like the Nursing Stress Scale have been valuable, their direct application in Iran may be inadequate due to unique cultural and organizational factors. Stressors and coping strategies may differ in the Iranian and Islamic cultural context, highlighting the need for a culturally grounded exploration rather than mere adaptation of existing scales (Sharif Nia et al., 2023).

Objectives

Research on improving the mental health of nurses and how that contributes to the health of patients generally should be a priority in Iran. This research is conducted with the objective of studying general dimensions of caring stress in nurses, particularly experiences of nurses employed in healthcare facilities where patients are met.

Methods

This study uses an exploratory sequential mixed-methods design to develop and validate a stress-management assessment scale for nurses. A qualitative phase informs item development, followed by a quantitative phase that assesses the scale's usability and validity in a larger sample.

In the present study, classical test theory will be used to develop and validate the scale (Vebrianto et al., 2020).

Review Phase

In the first step, a review study was conducted with the aim of examining the existing literature and enriching the findings from the interview phase. To achieve studies related to both the core concept of caring stress and the available scales in this area, a scoping review of databases was performed (Goudarzian et al., 2024).

Qualitative Phase

Conventional content analysis will be used in the next phase. Few concept analyses have been conducted on stress, and most existing scales focus on perceptions of stress, stressors, or consequences rather than caring stress (Sharif Nia et al., 2023).

Samples

In this phase, the research will focus on nurses working in various educational hospitals in Sari and Tehran, Iran. The researcher will select samples that provide rich information on caring stress management. Purposeful and maximum variation sampling will be used to ensure diverse perspectives. Participants will be selected based on demographic and professional characteristics such as age, gender, experience, and clinical setting. The qualitative sampling will include nurses from different specialties to capture a range of perspectives on caring stress management.

We will recruit nurses from various wards, hospitals, and regions to gather diverse perspectives on stress management. Data saturation will determine the sample size, ensuring thorough exploration of categories and themes across different contexts (Corbin et al., 2015).

Inclusion Criteria

The sample will consist of nurses working in educational hospital wards who meet the following criteria: (a) full responsibility for providing comprehensive, full-time patient care; (b) at least 1 year of work experience, indicating progression beyond the novice stage and sufficient exposure to stress-management demands (Mortimore et al., 2021); (c) no diagnosed psychological disorders or history of substance or alcohol use, as these factors may alter stress perception and coping; and (d) a bachelor's degree or higher, ensuring prior training in mental health. To ensure diversity and representativeness, key specialties (e.g., surgical, psychiatric, and emergency nursing) and a range of experience levels will be included, mirroring the qualitative phase.

Nurses will be excluded if they are on extended leave such as medical or maternity leave during the study period, work primarily in administrative, research, or teaching roles without direct patient care, or have language or communication difficulties that could interfere with interviews or questionnaires. Those experiencing acute illness or injury at the time of data collection, as well as individuals who decline to provide written informed consent, will also be excluded from participation.

Data Collection

In the initial qualitative phase, the researcher (AHG) will select participants based on inclusion criteria and provide explanations. Participants will give consent and agree on interview time and place. The purpose of the interview will be reiterated at the start, and written consent for audio recording and answering questions will be obtained. Confidentiality and secure handling of audio recordings will be assured. Participants can pause the interview if needed.

Participants will be asked for their contact information at the end of the interview for potential follow-up or verification. They are not required to answer all questions. Recordings will be transcribed verbatim immediately after each interview. The interviewer will carefully review the transcriptions and notes to understand participants’ experiences. Observations will be added to the transcripts for analysis. A semistructured interview guide with open-ended questions will be used to explore caring stress management.

Open-ended questions were included: (a) Could you talk about the care you provide for your patients? (b) What factors contribute to your stress regarding the care of your patients? (c) What are the symptoms and signs of stress that you experience as a result of patient care? (d) What strategies do you use to address or alleviate stress when caring for your patients? (e) How do you manage the stress that arises from caring for your patients?

Statistical Analysis

Data will be analyzed using conventional content analysis based on Elo and Kyngäs (2008) and will be managed with MAX-QDA version 10, treating each word or phrase as a unit of analysis. The analysis will follow three stages: selecting the unit of analysis and linking data to the research topic, transcribing and repeatedly reviewing interviews, and examining both explicit and implicit content, including text, pauses, and body language. Purposeful sampling with maximum variation will be used to ensure rich and relevant participant data (Assarroudi et al., 2018).

The researcher will use structured and unstructured matrices to categorize data, refining or creating categories as needed. An unconstrained matrix will capture core concepts of caring stress management for coding. Sampling, participant characteristics, data collection, and analysis will be documented, with operational definitions provided for main and subcategories (Elo & Kyngäs, 2008; Forman & Damschroder, 2007). Lincoln and Guba's criteria—credibility, dependability, confirmability, and transferability—will be used to ensure data quality (Speziale et al., 2011). Credibility will involve participant and peer review, dependability will be addressed by comparing data with other researchers, confirmability will be ensured through audio recording and review, and transferability will be strengthened by documenting the research context and including participants with diverse characteristics.

Quantitative Phase

At this stage of the study, the research population includes all nurses working in educational and therapeutic centers affiliated with the Tehran University of Medical Sciences (Tehran, Iran). A convenience sampling method will be used. Similar to the qualitative phase, the quantitative phase also employs a sampling approach aimed at achieving maximum variation, ensuring that participants represent a wide range of demographic and clinical backgrounds. The inclusion criteria were the same as those in the qualitative phase.

Data Collection

In the quantitative phase, a questionnaire developed during the qualitative phase will be used, which participants will complete in a self-reported format.

Face Validity

Qualitative method: This approach involves asking a small group of participants to review the questionnaire and provide feedback on the apparent suitability and clarity of the items. At least 15–20 nurses from the target population will be engaged in cognitive debriefing and face-validity interviews to check comprehensibility, relevance, and response functioning. They will be asked to provide feedback on the apparent relevance of the questionnaire's name and items, understandability, readability, difficulty level, comfort with the items, clarity, and acceptability of the questionnaire (Polit & Yang, 2015).

Quantitative method: After making suggested revisions based on the qualitative feedback, quantitative face validity will be measured by calculating the “item impact score.” For this phase, at least 15–20 nurses from the target population will rate each item using a Likert scale: (5: Very Important, 4: Somewhat Important, 3: Moderately Important, 2: Slightly Important, 1: Not Important). The item impact score for each item is calculated by multiplying the importance rating by its frequency. An item impact score >1.5 is considered acceptable, a threshold based on an average score of 3 and a 50% frequency rate (Rust & Golombok, 2014).

Content Validity

Qualitative method: In this approach, content validity will be evaluated based on criteria such as the wording of items, use of appropriate terminology, item placement, suitability of the response scale, appropriate scoring, clarity, and simplicity of items. This evaluation will be conducted with input from 12 to 15 experts (Polit & Beck, 2008).

To ensure the rigor of this process, the selection of experts was based on a combination of their academic qualifications and professional experience. The panel will consist of experts from diverse backgrounds, including two psychometricians with a minimum of 10 years of experience in scale development, three nursing educators with doctoral degrees and extensive clinical experience, and two senior clinical nurses with more than 15 years of experience in different specialties (e.g., intensive care and surgery). This diverse panel ensured that the scale's content was evaluated not only for its theoretical relevance but also for its practical applicability and clinical appropriateness.

Quantitative method: To assess quantitative content validity, the questionnaire will be reviewed by 10 experts, who will calculate the content validity ratio (CVR) and content validity index (CVI; Allahyari et al., 2011). The CVR assesses the necessity of each item based on the consensus of a panel of experts (Polit et al., 2007). Lawshe's formula is used to calculate CVR as follows:

CVR=nE−N/2N/2

In this formula, nE represents the number of experts who consider the item essential, and N is the total number of experts on the panel. Each item is rated on a three-point scale as “essential,” “useful but not essential,” or “not essential.” Lawshe's table provides the minimum acceptable CVR value for statistical significance (P < .05), depending on the number of experts. If the calculated CVR exceeds the table value, the item is deemed necessary and important for the instrument with an acceptable level of statistical significance.

The CVI is calculated to answer whether the items in the scale are optimally designed to measure the intended content. It includes two types: I-CVI (item-content validity index) and S-CVI (scale-content validity index), both of which will be calculated in this study (84). I-CVI represents the CVI calculated for each individual item in the scale, while S-CVI is the CVI calculated for the entire scale. To calculate the I-CVI, 10 experts will rate each item's relevance on a 4-point Likert scale (1: Not relevant, 2: Somewhat relevant, 3: Quite relevant, 4: Highly relevant). I-CVI is determined by dividing the number of experts who rate the item as a 3 or 4 by the total number of experts. A score of 0.90 is considered excellent, with 0.80 as the minimum acceptable threshold for I-CVI (Polit & Beck, 2006).

S-CVI is the ratio of items that receive a rating of 3 or 4 from at least two experts (the minimum number required). When the S-CVI value is equal to 0.50, it means that both experts believe that 5 out of 10 items are relevant. A score of 1 indicates that both experts agree that all items are relevant, and values of 0.80 and above are considered acceptable (Polit et al., 2007). Additionally, the kappa coefficient (K) serves as an important complement to the CVI, indicating the degree of chance agreement among the experts. A kappa index of above 0.74 is considered excellent, a range of 0.74 to 0.60 is considered good, and a range of 0.59 to 0.40 is considered fair (Polit et al., 2007).

Construct Validity

In this study, exploratory (EFA) and confirmatory factor analyses (CFA), as well as convergent and discriminant validity, will be used to assess construct validity. The required sample size for EFA and CFA will be determined based on 10 participants per item, using a convenience sampling method.

In this study, EFA will first be conducted using the maximum likelihood extraction approach. Specifically, we will target a minimum of 10 participants per item with an absolute minimum sample of 200 respondents for EFA. To assess the quality of responses and the quality of samples in EFA, the Kaiser–Meyer–Olkin (KMO) index and Bartlett's test of sphericity will be calculated, with an acceptable KMO value being >0.60 (Polit & Yang, 2015). To determine the appropriate number of extracted factors, a modern parallel analysis approach will be employed.

The structure of the construct obtained through EFA will be examined using CFA. For CFA, we will use an independent sample of at least 200 participants (Hox, 2021). In this method, relevant variables and indicators are initially selected based on the underlying theory, and then CFA is utilized to determine whether these variables and indicators load onto the predicted factors as expected, or if their composition has changed and they have loaded onto different factors.

The most common goodness-of-fit indices for the proposed model will be obtained based on the accepted thresholds using maximum likelihood estimation. The most common indices will be evaluated through CFA. The assumption of normality will be examined based on skewness and kurtosis indices of ±3 and ±7, respectively (West et al., 1995). Hooper et al. (2008) state that there is no golden rule for assessing model fit, and it is better to report multiple indices. In this study, based on some sources, the goodness-of-fit indices that will be examined include the chi-square index (CMIN), the root mean square error of approximation, the comparative fit index, the normalized fit index, the adjusted goodness-of-fit index, and finally the chi-square to degrees of freedom (DF) ratio (CMIN/DF; Meyers et al., 2016).

Convergent and Discriminant Validity

In this method, to determine convergent and discriminant validity, the correlation between variables will first be calculated using SPSS/AMOS24 software, followed by the establishment of a standardized regression weighting table. Finally, convergent and discriminant validity will be obtained using the Excel coding tool provided by Gaskin (2012).

The convergent and discriminant validity of the construct of caring stress management in nursing will be assessed using the Fornell and Larcker’s (1981) approach based on the following parameters: (a) average variance extracted (AVE), (b) maximum shared squared variance (MSV), and (c) composite reliability (CR). To establish convergent validity, the AVE must be >0.5, while for confirming discriminant validity, the MSV must be less than the AVE. Additionally, a CR of the final construct >0.7 will be considered acceptable (Munro, 2005).

Reliability

In this study, the reliability of the scale will be assessed using internal consistency methods, specifically Cronbach's alpha, McDonald's omega coefficient (Ω), and the average interitem correlation. To do this, the developed scale will be administered to 30 nurses, and the internal consistency will be determined by calculating Cronbach's alpha coefficient. The Cronbach's alpha coefficient reflects the relationship of each item with other items and the overall score items have equal variance, Cronbach's alpha is calculated using the following formula (Vaske et al., 2017):

Nr¯(1+(N−1)r¯)

where N is the number of items and r is the average interitem correlation. A value of α of 0.80 or higher is considered good, while 0.70 or higher is deemed acceptable. Also, for McDonald's omega coefficient, a value of 0.70 or higher will also be considered satisfactory (Mohammadbeigi et al., 2015).

In this study, to determine stability, external consistency or the intraclass correlation coefficient will be employed. For this purpose, the test–retest method will be utilized in the following way: 30 nurses will be conveniently selected and asked to complete the researcher-developed questionnaire. This process will be repeated 2 weeks later under the same conditions (the same individuals in the same environment), and the correlation between the scores obtained from the two tests will then be assessed (Moreno-Jiménez et al., 2014).

To assess the accuracy of measurement in absolute reliability, the measurement error of the criterion will be utilized. The formula for calculating the measurement error is as follows (Yuan & Kelly, 2019):

Smeasurement=SDtest1−ICC

Responsiveness

In addition to the two key indicators of validity and reliability, the responsiveness of the scale is also an important criterion for a good and appropriate instrument.

To calculate the responsiveness rate of this instrument, standard error measure (SEM), smallest detectable change, or/and minimum detectable change (MDC) will be employed (Polit & Yang, 2015). To do this, the following formulae were used:

SEM=SD×(1−R)
MDC95=SEM×2×1.96

Interpretability

In this study, to assess the interpretability, the designed scale will be administered to a selected group of nurses at two different time points, and the extent of score changes as well as the scale's interpretability will be evaluated.

Scoring Method

Linear transformation will be employed for scoring the scale. The formula used for this transformation is:

T(x)=x−xminxmax−xmin

In this formula, x represents the raw score from the scale, xmin is the minimum possible raw score, and xmax is the maximum possible raw score. The transformed score, T(x), provides a consistent and easily interpretable value, allowing for direct comparison of results. This approach ensures that the scale's output is standardized, which is crucial for subsequent statistical analysis and interpretation.

Discussion

This paper presents the design and psychometric evaluation of the Caring Stress Management Scale for nurses, addressing a gap in existing stress management scales. The study used a sequential mixed-methods approach, starting with qualitative exploration followed by quantitative validation to ensure the scale captures stressors unique to nursing. While general work demands contribute to stress, intensive patient interactions are the primary source, which current scales fail to capture. The new scale will provide a validated measure of stress and management practices among nurses, particularly within the Iranian context.

The complexities of patient care impose significant stress on nurses, affecting their health, job satisfaction, and quality of care (Goudarzian et al., 2024). The Caring Stress Management Scale will identify stressors specific to nursing and assess strategies used to manage them. Consistent with international findings on chronic stress, burnout, and decreased job satisfaction (Allen, 2018; Shen et al., 2020), the scale aims to guide interventions that reduce caring stress and improve outcomes for both nurses and patients.

The inclusion of cultural considerations in the scale's development is also noteworthy. This study recognizes that Iranian nurses may experience unique stressors due to cultural expectations and healthcare infrastructure limitations (Shamsi & Peyravi, 2020). Tailoring the scale to account for these factors ensures that it is contextually relevant and sensitive to the Iranian nursing population, thus enhancing its validity and utility within this group. Furthermore, the sequential mixed-methods design allows for a thorough exploration and validation process, with qualitative findings enriching the quantitative phase, resulting in a more comprehensive and representative scale.

This methodological rigor of the study, along with the various validity assessments—content, construct, convergent, and discriminant—in addition to the reliability measures regarding internal consistency and test–retest, gives more strength to the Caring Stress Management Scale as a potentially reliable instrument. Such a scale may open ways for future studies on stress management among nurses and bring insights into effective strategies for stress reduction. The study also forms the basis for adapting the scale in other healthcare contexts and sets, as well as those facing similar challenges, hence contributing significantly to occupational health literature in nursing.

Implications for Nursing Practice

The scale has given nurses and managers of health a specific scale to identify caring stressors, enabling targeted interventions. This will improve nurses’ mental health, job satisfaction, and may prevent burnout, which are all considered necessary for the sustainability of good caring practices and quality patient care. Since the evaluation of specific stress factors related to caring, healthcare institutions may implement culturally more appropriate and occupation-specific stress management strategies. As the study suggests, such interventions may be most effective in settings with specific cultural or organizational stressors, as was witnessed among Iranian nurses.

In fact, the findings from this scale can really guide policy reforms in the improvement of the working conditions of nurses. Recognizing that caring stress may affect job performance and patient safety, policies can be made by healthcare organizations to ensure adequate staffing, availability of resources, and support for mental health. A reduction in caring stress has a positive influence on patient outcomes. Less-stressed nurses would probably provide an increase in the quality of care and safety for patients since better-managed stress would contribute to better focus, decision-making, and empathy in care interactions.

Conclusions

The development and psychometric evaluation of the Caring Stress Management Scale provide a significant step forward for understanding the specific stressors faced by nurses in caring roles. This scale gives meaning to stress that pertains to the care of patients, which was an important gap in the scales available; thus, it is a culturally appropriate scale designed for Iranian nurses.

Acknowledgments

The authors acknowledge student research committee of the Mazandaran University of Medical Sciences (Sari, Iran) that helped to carry out this study.

Footnotes

ORCID iD: Amir Hossein Goudarzian https://orcid.org/0000-0002-3266-101X

Ethical Considerations: The study objectives and procedures were fully explained to the nurses, and they were assured that participation is voluntary. In addition, online informed consent was obtained from each participant. All participants were assured of the confidentiality of the data and findings that will be reported and published. This study is based on a nursing PhD thesis, and the ethics committee of the Tehran University of Medical Sciences approved the protocol of this study (code: IR.TUMS.FNM.REC.1402.137).

Author Contributions: AHG, HS-N, and EN designed the study. All of the authors contributed equally to writing the draft of manuscript. ANN and HS-N, and EN critically revised the manuscript. All of the authors approved the final version of the manuscript.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement: Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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