Abstract
Background
Teachers frequently perform extensive instructional and non-instructional duties that may impair their work–life balance (WLB). In Nigeria, although workload, stress, and job satisfaction among teachers have been described, limited attention has been given to WLB, particularly across rural and urban settings. The aim of the study was to analyze associations between work conditions and WLB among secondary school teachers in Southwest Nigeria.
Methods
A quantitative, survey-based, analytical cross-sectional study was conducted in 2018 among secondary school teachers selected through multi-stage sampling across three urban and four rural Local Government Areas in Oyo State. Data were collected using a semi-structured, self-administered questionnaire that captured socio-demographic characteristics, work conditions, and WLB, measured across three dimensions: work interference with personal life, personal life interference with work, and work/personal life enhancement. Multilevel linear regression models with random intercepts for school location and school were used to assess associations between occupational characteristics and WLB, adjusting for age, gender, marital status, and school type.
Results
We recruited 1,174 teachers, 49.2% from rural and 50.8% from urban areas; most were female (61.4%) and employed in public schools (71.3%). A higher proportion of rural teachers were younger, < 40 years (51.2%), worked < 8 h per day (43.2%), and taught ≥ 50 students per class (42.0%) than their urban counterparts (43.6%, 27.7%, 27.0% respectively). However, no important rural–urban differences were observed in WLB. Overall, teachers who taught ≥ 3 subjects (mean difference compared with < 3 subjects= -5.2, 95% CI: -7.3; -3.1), worked ≥ 8 h daily (mean difference compared with < 8 h = -4.5, 95% CI: -6.5; -2.6), or stood ≥ 4 h at work (mean difference compared with < 4 h, = -2.4, 95% CI: -4.1; -0.8) had lower WLB scores. Teaching classes with ≥ 50 students (mean difference compared with < 50 students = + 5.4, 95% CI: +3.3; +7.4) was associated with higher WLB scores.
Conclusions
Work–life balance among teachers in Southwest Nigeria was influenced by work conditions. High subject load, long working hours, and prolonged standing negatively impacted WLB. School location (rural vs. urban) was not a significant determinant of WLB. Interventions targeting workload redistribution, ergonomic improvements, and administrative support are essential for enhancing teacher well-being and performance.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28543-2.
Keywords: Work–life balance, Teachers, Occupational characteristics, Work conditions, Rural–urban differences, Nigeria, Secondary schools, Workload, Well-being
Introduction
Work-life balance is defined as “satisfaction and smooth functioning at work and home without any role conflict” [1]. It is described as the stability between professional responsibilities and personal life [2], which is essential for improving productivity, efficiency, and overall well-being [3–5]. Teachers’ responsibilities extend beyond the classroom, encompassing lesson planning, grading, administrative duties, and other non-teaching activities [6–8]. These diverse duties significantly contribute to their overall workload, which can consequently impact on their work-life balance.
Poor WLB among teachers can lead to burnout, job dissatisfaction, and decreased teaching effectiveness [7–10], The economic cost of poor WLB among teachers is significant, as it can lead to increased absenteeism, high turnover rates, and reduced quality of education, thereby impeding national economic growth and workforce development [ [9, 11]; [12, 13, 14]. Policies and programs that enhance teachers’ WLB can have long-term benefits for both educational systems and national economies [10, 12, 15]. The struggle to achieve an optimal work-life balance is a pervasive concern for employees across various sectors, including education, primarily due to the interplay of specific individual needs and limited available resources [16]. This struggle is particularly acute for educators, whose profession inherently demands a significant investment of time and emotional labor beyond conventional working hours [10, 17]. This pervasive challenge can lead to professional dissatisfaction, impacting both their professional and private lives [13]. Some studies have investigated the work conditions, workload, and job satisfaction of teachers in urban areas and rarely in the rural areas.
The location of a school (rural or urban area) can influence a teacher’s professional and personal life consequently affecting their work-life balance [18, 19]. Teachers in rural schools often face unique challenges that can significantly affect their work-life balance. Such challenges include increased responsibilities and workload [18, 19], retention issues [20] and limited resources [21]. In rural settings, teachers may experience isolation [19]. This isolation can limit their access to professional development opportunities, peer support networks, and personal amenities, potentially leading to increased stress and reduced well-being [19]. A study conducted among primary and elementary teachers in rural and urban regions of the province of Newfoundland and Labrador, Canada revealed that there were differences in the workloads of teachers in both regions which could ultimately affect their WLB [22]. While some studies have investigated the work conditions, workload, and job satisfaction of teachers in rural and urban areas, they have seldom directly assessed their work-life balance.
In Nigeria, researchers have reported work conditions, workload, work related stress and job satisfaction of teachers [23–26]. Some researchers also investigated impact of WLB on Job performance, Job satisfaction and teacher engagement [27–29]. Nnubia, Ibeanu, and Okechukwu (2022) investigated socio-economic and household characteristics associated with work-family conflict among female primary school teachers in Enugu State, Nigeria [30]. However, studies on work conditions associated with work-life balance of teachers are lacking. In Lagos, academic staff of University of Lagos reported poor work-life balance [31]. Despite these insights, significant gaps remain in understanding the work conditions influencing WLB among teachers in general and specifically in rural and urban areas of Nigeria [32, 33].
Limited research has examined how sociodemographic characteristics and work conditions impact teachers’ ability to balance work and personal life in rural and urban areas. Addressing these gaps is essential for informing policies and intervention programs aimed at improving work conditions, job satisfaction and overall well-being of teachers [4, 34–36]. There is a need to determine factors associated with WLB of teachers which consequently affect their performance and student outcomes in both rural and urban areas.
The aim of the study was to analyze associations between work conditions (determinants) and WLB (effect) among school teachers in Oyo State, one of the 6 states in southwest Nigeria. Rural and urban school location was examined as an analytical variable alongside specific occupational exposures. The findings could contribute to strategies for improving work-life balance among teachers of Oyo State and Nigeria at large, with the definitive goal of fostering economic productivity, improved health outcomes, and better quality of life for teachers.
Materials and methods
Study design
In 2018, we conducted a quantitative, survey-based, analytical cross-sectional study among secondary school teachers in seven Local Government Areas (LGAs) in Oyo State, Southwest Nigeria, using a multi-stage probability sampling design.; three urban and four rural LGAs. The three urban LGAs were Ibadan Southwest, Ibadan Southeast, and Ibadan Northwest LGAs while the four rural LGAs were Ido, Egbeda, Akinyele, and Oluyole LGAs.
Participants, sample size and recruitment
The sample size for the study was calculated using the formula for comparing two independent proportions [37]. n = (Zα/2 + Zβ)² × [P₁(1 − P₁) + P₂(1 − P₂)] / (P₁ − P₂)² where Zα/2 = 1.96 (two-sided 5% significance level), Zβ = 0.84 (80% power), P₁ = proportion in the rural group, and P₂ = proportion in the urban group, based on estimates derived from published literature on teacher job satisfaction in Nigeria. This yielded a minimum sample size of 490 per group. To account for a non-response rate of 10%, the minimum was increased to 544 per group.
The sample comprised male and female secondary school teachers from public and private schools. Oyo state has a population of about 8 million and is divided into 33 Local Government Areas (LGAs), 21 rural and 12 urban. Given the large number of LGAS and schools, a multi-stage sampling technique was used to select sample units [37]. In the first stage, 3 out of 12 urban LGAs and 4 out of 21 rural LGAs were selected using a simple random sampling technique [37]. In the second stage, schools in each LGA were selected by school type (public and private) using a simple random sampling technique. The number of schools selected from each LGA was proportionate to the total number of registered schools in that LGA, using lists obtained from the Oyo State Universal Basic Education Board (SUBEB) as the sampling frame. Simple random sampling was then applied within each stratum to select specific schools.
A total of 24 schools in 4 rural LGAs and 25 schools in 3 urban LGAs of Ibadan were selected.
We contacted schools’ Principals and obtained consent to participate.
The data were collected within six weeks between May and June 2018. Teachers completed a self-administered, semi-structured questionnaire capturing socio-demographic characteristics, occupational data, family life, and work-life balance.
Inclusion and exclusion criteria
Inclusion criteria: Teachers who were on the full-time payroll and had been teaching at the respective schools for at least one year before the study were included in the study.
Exclusion criteria: Those who had only administrative roles in the school were excluded.
Assessment instruments
A semi-structured questionnaire (Supplementary File 1) was developed and used to obtain information on sociodemographic characteristics, occupational characteristics, Work-Life balance of Secondary school teachers.
Work-life balance was measured with a 15-item scale adapted by Hayman [38] from a validated instrument developed by Fisher-McAuley, et al. in 2003 [39] to assess three dimensions of work-life balance [38]: work interference with personal life (WIPL), personal life interference with work (PLIW), and work/personal life enhancement (WPLE).
Work Interference with Personal Life (WIPL)
This includes work related factors that impact an individual’s personal life. It measures the impact of work on personal life [38]. : The seven items measured by this construct are: (1) My personal life suffers because of work, (2) My job makes personal life difficult, (3) I neglect personal needs because of work, (4) I put personal life on hold for work, (5) I miss personal activities because of work, (6) I struggle to juggle work and non-work, and (7) I am happy with the amount of time for non-work activities.
Personal Life Interference with Work (PLIW)
This is the impact of or the interference of the personal life on work. It measures the reverse phenomenon, the impact of personal life on the work of individuals [38]. The four items measured are: (1) My personal life drains me of energy for work, (2) I am too tired to be effective at work, (3) My work suffers because of my personal life, and (4) It is hard to work because of personal matters.
Work Personal Life Enhancement (WPLE)
This explains how work and personal life enhance each other. The items help understand the support and enhancement provided by work on personal life and vice-versa [38]. The four items measured are: (1) My personal life gives me energy for my job, (2) My job gives me energy to pursue personal activities, (3) I have a better mood at work because of personal life, and (4) I have a better mood because of my job.
Each item was rated on a 5-point Likert scale (Strongly Agree = 1 to Strongly Disagree = 5). For WIPL and PLIW, higher scores indicate less interference with personal life or work, respectively, reflecting better WLB. For WPLE, items were reverse-scored (Strongly Agree = 5, Strongly Disagree = 1), such that higher scores indicate greater work–personal life enhancement and therefore better WLB. This ensures that for the composite total WLB score, a higher total score consistently reflects better overall work-life balance across all three dimensions [38]. The last item on the WIPL dimension was reverse scored. WIPL has 7 items (score range 7–35); PLIW and WPLE each have 4 items (score range 4–20 each). The total WLB score was obtained using the three dimensions. The total Work-life balance score was computed by summing up all the responses to the individual items in the three dimensions. The minimum total WLB score was 15 and the maximum score was 75. Higher scores indicated better work-life balance, while lower scores indicated poorer work-life balance. Mean scores and standard deviation were calculated.
In addition to the WLB dimensions, the questionnaire included items on perceived work-related stress, specifically the most stressful aspect of the teaching job (e.g., writing lesson notes, marking and recording of scripts). These items were treated as occupational descriptors and are reported in Table 2 as part of the occupational characteristics; they were not used as primary outcome variables in the regression analysis.
Table 2.
Occupational characteristics and location of secondary school teachers in Southwest Nigeria
| Variable | Total n (%) |
Rural n (%) |
Urban n (%) |
P-value* |
|---|---|---|---|---|
| Total | 1174 (100) | 578 (100) | 596 (100) | |
| Years engaged in teaching at secondary school level | ||||
| < 10 years | 682 (58.1) | 358 (61.9) | 324 (54.4) | 0.05 |
| 10–19 years | 307 (26.1) | 139 (24.1) | 168 (28.2) | |
| ≥ 20 years | 184 (15.7) | 81 (14.0) | 103 (17.3) | |
| Missing | 1 (0.1) | 0 (0.0) | 1 (0.1) | |
| Hours spent at work daily | ||||
| < 8 h | 415 (35.3) | 250 (43.2) | 165 (27.7) | < 0.001 |
| ≥ 8 h | 759 (64.7) | 328 (56.8) | 431 (72.3) | |
| Hours spent standing while at work | ||||
| ≤ 4 h | 491 (41.8) | 246 (42.6) | 245 (41.1) | 0.68 |
| > 4 h | 673 (57.3) | 326 (56.4) | 347 (58.2) | |
| Missing | 10 (0.9) | 6 (1.0) | 4 (0.7) | |
| School subjects taught | ||||
| < 3 school subjects | 821 (69.9) | 411 (71.1) | 410 (68.8) | 0.39 |
| ≥ 3 school subjects | 352 (30.0) | 166 (28.7) | 186 (31.2) | |
| Missing | 1 (0.1) | 1 (0.2) | 0 (0) | |
| Classes taught | ||||
| < 6 classes | 816 (69.5) | 394 (68.2) | 422 (70.8) | 0.36 |
| ≥ 6 classes | 358 (30.4) | 184 (31.8) | 173 (29.0) | |
| Missing | 1 (0.1) | 0 (0.0) | 1 (0.2) | |
| Students per class | ||||
| < 50 students | 767 (65.3) | 332 (57.4) | 435 (73.0) | < 0.001 |
| ≥ 50 students | 404 (34.4) | 243 (42.0) | 161 (27.0) | |
| Missing | 3 (0.3) | 3 (0.5) | 0 (0) | |
| Have another job apart from this teaching job | ||||
| Yes | 211 (18.0) | 119 (20.6) | 92 (15.4) | 0.02 |
| No | 950 (80.9) | 453 (78.4) | 497 (83.4) | |
| Missing | 13 (1.1) | 6 (1.0) | 7 (1.2) | |
| The most stressful aspect of your job | ||||
| No stress/none | 143 (12.2) | 71 (12.3) | 72 (12.1) | 0.80 |
| Writing lesson note/lesson plan | 404 (34.4) | 197 (34.1) | 207 (34.7) | |
| Marking and recording of scripts/register | 414 (35.3) | 200 (34.6) | 214 (35.9) | |
| Others | 212 (18.0) | 109 (18.9) | 103 (17.3) | |
| Missing | 1 (0.1) | 1 (0.2) | 0 (0.0) | |
| Satisfaction with teaching job | ||||
| Very satisfied | 318 (27.1) | 168 (29.1) | 150 (25.2) | 0.53 |
| Satisfied | 719 (61.2) | 344 (59.5) | 375 (62.9) | |
| Dissatisfied | 113 (9.6) | 53 (9.2) | 60 (10.1) | |
| Very dissatisfied | 19 (1.6) | 11 (1.9) | 8 (1.3) | |
| Missing | 5 (0.4) | 2 (0.3) | 3 (0.5) | |
*Rural vs. urban, chi-squared tests
Statistical analysis
We used chi-squared tests to compare distributions of categorical variables between rural and urban teachers. To evaluate the association between total WLB, WIPL, PLIW, and WPLE values and occupational characteristics, we fitted crude and multivariable maximum likelihood multilevel models (also known as mixed, hierarchical, or random-coefficient models) [40]. We chose this method because it reflects the sampling strategy (individual observations nested within school nested within location) and also because we assumed outcome (WLB) correlations either within location (rural or urban) or within schools; therefore, we fitted three-level nested linear regression models with random intercepts for the two areas (rural and urban) and for the 49 schools, to calculate mean differences and 95% confidence intervals (CI).
In multivariable models, we included as potential confounders gender, age, marital status, and type of school; these variables were selected based on a directed acyclic graph (DAG).
Before running the above-mentioned analyses, in preliminary analyses, we had verified with two-level multivariable linear regression models that there were no or negligible differences between rural and urban schools in WLB and its dimensions.
Data management and analysis were done using SPSS version 20 and Stata 19 (StataCorp. 2025).
Ethical approval
Ethical approval was obtained from the Oyo State Ministry of Health Ethical Review Committee.
(Ethics approval number AD13/479/437). This study was conducted in accordance with the Declaration of Helsinki (for human subjects research) [41] and the National Code for Health Research Ethics in Nigeria [42].
Clinical trial number
Not Applicable.
Results
Sociodemographic characteristics
A total of 1174 teachers, 578 (49.2%) in rural and 596 (50.8%) in urban areas, were recruited across the study Local Government Areas (LGAs). Most (721, 61.4%) were females, teaching in public schools (837,71.3%), and had attained tertiary education (98.3%). Teachers in rural schools were younger than those in urban schools, while marital status was similar. About half of the teachers in urban areas were from schools in Oluyole/Ibadan Southwest. There were more private schools in urban areas. (Table 1)
Table 1.
Sociodemographic characteristics and location of secondary school teachers in Southwest Nigeria
| Variable | Total n (%) |
Rural n (%) |
Urban n (%) |
|---|---|---|---|
| Total | 1174 (100) | 578 (100) | 596 (100) |
| Gender | |||
| Female | 721 (61.4) | 365 (63.1) | 356 (59.7) |
| Male | 449 (38.2) | 211 (36.5) | 238 (40.0) |
| Missing | 4 (0.3) | 2 (0.4) | 2 (0.3) |
| Age | |||
| < 40 years | 556 (47.4) | 296 (51.2) | 260 (43.6) |
| ≥ 40 years | 598 (50.9) | 274 (47.4) | 324 (54.4) |
| Missing | 20 (1.7) | 8 (1.4) | 12 (2.0) |
| Marital Status | |||
| Married | 977 (83.2) | 478 (82.7) | 499 (83.7) |
| Single | 197 (16.8) | 100 (17.3) | 97 (16.3) |
| Local Government Area | |||
| Akinyele | 97 (8.3) | 72 (12.5) | 25 (4.2) |
| Egbeda/Ibadan South East | 258 (22.0) | 163 (28.2) | 95 (15.9) |
| Ido/Ibadan North West | 334 (28.4) | 167 (28.9) | 167 (28.0) |
| Oluyole/Ibadan South West | 485 (41.3) | 176 (30.4) | 309 (51.9) |
| Type of school | |||
| Public | 837 (71.3) | 432 (74.7) | 405 (67.9) |
| Private | 337 (28.7) | 146 (25.3) | 191 (32.1) |
Occupational characteristics
Overall, 58.1% were engaged in teaching for < 10 years, 64.7% spent ≥ 8 h daily at work and 57.3% spent > 4 h standing while at work, 69.9% taught < 3 subjects, 69.5% taught < 6 classes and 65.3% taught < 50 students in a class. The majority, 80.9%, did not engage in any jobs other than teaching, and 61.2% were satisfied with their jobs. There were rural-urban differences in their work conditions; more teachers in the rural areas had been teaching in secondary schools for < 10years, at work for < 8 h, taught ≥ 50 students per class, and had other jobs compared to teachers in the urban areas. (Table 2)
Association between teachers’ occupational characteristics and total WLB score
School location (rural or urban) had no or negligible influence on WLB (results not shown). Overall, the average WLB Score was 49.2 (SD 15.1). Teachers who taught ≥ 3 school subjects (43.3 ± 12.8), worked for ≥ 8 h daily (47.0 ± 15.0) and spent ≥ 4 h standing while at work (47.4 ± 15.2) had lower WLB mean scores; conversely, WLB score was higher when there were ≥ 50 students per class (54.4 ± 14.5) (Table 3).
Table 3.
Association between teachers’ occupational characteristics and total WLB score
| Independent variable | Dependent variable Mean (SD) |
P-value* | Adjusted mean difference** | 95% CI** |
|---|---|---|---|---|
| Total WLB Score | ||||
| School subjects taught | ||||
| < 3 school subjects | 51.7 (15.5) | < 0.001 | 0.0 | Reference |
| ≥ 3 school subjects | 43.3 (12.8) | -5.2 | -7.3; -3.1 | |
| Students per class | ||||
| < 50 students | 46.4 (14.9) | < 0.001 | 0.0 | Reference |
| ≥ 50 students | 54.4 (14.5) | + 5.4 | + 3.3; +7.4 | |
| Hours spent at work daily | ||||
| < 8 h | 53.0 (14.7) | < 0.001 | 0.0 | Reference |
| ≥ 8 h | 47.0 (15.0) | -4.5 | -6.5; -2.6 | |
| Hours spent standing while at work | ||||
| < 4 h | 51.5 (14.8) | 0.01 | 0.0 | Reference |
| ≥ 4 h | 47.4 (15.2) | -2.4 | -4.1; -0.8 | |
*From crude multilevel models (with random intercepts for school location and school)
**From multivariable multilevel models (with random intercepts for school location and school) adjusted for gender, age, marital status, and type of school
Association between occupational characteristics and dimensions of WLB
The three components of WLB score (WIPL, PLIW, and WPLE) showed patterns similar to that observed for total WLB score: lower scores which indicates poor work-life balance was associated with 3 or more school subjects taught, 8 or more hours spent daily at work, and 4 or more hours spent standing while at work; higher scores when there were > = 50 students per class (Table 4).
Table 4.
Association between teachers’ occupational characteristics and dimensions of WLB score
| Independent variable | Dependent variable Mean (SD) |
P-value* | Adjusted mean difference** | 95% CI** |
|---|---|---|---|---|
| WIPL Score | ||||
| School subjects taught | ||||
| < 3 school subjects | 23.3 (5.1) | < 0.001 | 0.0 | Reference |
| ≥ 3 school subjects | 20.7 (5.1) | -2.4 | -3.2; -1.6 | |
| Students per class | ||||
| < 50 students | 22.0 (5.2) | < 0.001 | 0.0 | Reference |
| ≥ 50 students | 23.5 (5.2) | + 1.4 | + 0.7; +2.2 | |
| Hours spent at work daily | ||||
| < 8 h | 23.6 (5.0) | < 0.001 | 0.0 | Reference |
| ≥ 8 h | 22.0 (5.3) | -1.6 | -2.3; -0.8 | |
| Hours spent standing while at work | ||||
| < 4 h | 23.1 (5.3) | 0.01 | 0.0 | Reference |
| ≥ 4 h | 22.1 (5.2) | -0.8 | -1.4; -0.2 | |
| PLIW Score | ||||
| School subjects taught | ||||
| < 3 school subjects | 14.4 (3.6) | < 0.001 | 0.0 | Reference |
| ≥ 3 school subjects | 12.9 (3.8) | -1.5 | -2.0; -0.9 | |
| Students per class | ||||
| < 50 students | 13.7 (3.8) | 0.006 | 0.0 | Reference |
| ≥ 50 students | 14.6 (3.5) | + 0.8 | + 0.2; +1.3 | |
| Hours spent at work daily | ||||
| < 8 h | 14.4 (3.5) | 0.01 | 0.0 | Reference |
| ≥ 8 h | 13.7 (3.8) | -0.7 | -1.2; -0.2 | |
| Hours spent standing while at work | ||||
| < 4 h | 14.6 (3.7) | < 0.001 | 0.0 | Reference |
| ≥ 4 h | 13.5 (3.6) | -0.8 | -1.3; -0.4 | |
| WPLE Score | ||||
| School subjects taught | ||||
| < 3 school subjects | 14.0 (3.2) | 0.04 | 0.0 | Reference |
| ≥ 3 school subjects | 13.5 (2.9) | -0.4 | -0.8; +0.06 | |
| Students per class | ||||
| < 50 students | 13.7 (2.9) | 0.02 | 0.0 | Reference |
| ≥ 50 students | 14.2 (3.3) | + 0.5 | + 0.04; +0.9 | |
| Hours spent at work daily | ||||
| < 8 h | 14.1 (3.1) | 0.02 | 0.0 | Reference |
| ≥ 8 h | 13.7 (3.1) | -0.4 | -0.8; +0.03 | |
| Hours spent standing while at work | ||||
| < 4 h | 14.3 (3.0) | 0.001 | 0.0 | Reference |
| ≥ 4 h | 13.6 (3.1) | -0.6 | -1.0; -0.3 | |
*From crude multilevel models (with random intercepts for school location and school)
**From multivariable multilevel models (with random intercepts for school location and school) adjusted for gender, age, marital status, and type of school
Discussion
In this study among secondary school teachers in Southwest Nigeria, we found that work–life balance is influenced by work conditions: high subject load, long working hours, and prolonged standing negatively impact WLB. Interestingly, teachers with more students per class showed a higher WLB score.
Workload and subject load
Teachers who taught more than three subjects had significantly poorer WLB, indicating that excessive subject allocation increases job demands, reduces recovery time, and limits engagement in family or social activities [43]. This aligns with earlier reports that excessive workload—driven by curriculum demands and teacher shortages—negatively affects teachers’ psychosocial health and family functioning [7, 8]. Ogboro and Nwadiani (2017) reported high workload of teachers due to increased student enrollment and the necessity for teachers to teach more subjects, which can lead to poor work-life balance [44].
Similar patterns have been reported where teachers cited excessive workload and class preparation demands as primary factors reducing their work–life satisfaction [45]. The overutilization of teachers to manage large enrolments and multiple subjects increases cognitive and emotional fatigue, leading to reduced job satisfaction, burnout, and work–family conflict [7, 14, 46].
Working hours
In Nigeria, Chana, Ibrahim, and Abdulhamid [27] reported that long working hours and heavy workloads were among the primary factors undermining work-life balance and job satisfaction of business studies teachers in Bauchi State. Similarly, Ademola, Tsotetsi, and Gbemisola [28] found that extended working hours adversely affected teachers’ job performance in Nigerian secondary schools — a pattern consistent with the findings of the present study. Teachers who worked more than eight hours per day reported lower WLB scores, consistent with international literature linking long working hours to diminished personal time, fatigue, and strained family relationships [7, 47]. Extended hours are a well-documented source of occupational stress among educators, with prolonged exposure leading to emotional exhaustion and decreased motivation [48].
Canadian teachers similarly reported that long working hours negatively affected their personal relationships and social interactions, leaving them too exhausted to engage in non-work activities [48]. These findings reinforce that temporal imbalance—when work demands consistently exceed personal recovery time—is a key determinant of poor work–life balance among teachers.
Physical workload and standing time
Teachers who spent more than four hours standing daily also exhibited poorer WLB. Prolonged standing is associated with musculoskeletal strain, particularly low back and lower-limb pain [49, 50]. The physical fatigue resulting from long-standing periods not only affects classroom performance but also spills over into personal life, limiting participation in leisure or family activities [48]. This physical strain may contribute to occupational fatigue and discomfort, which in turn exacerbate work–life imbalance [50]. Therefore, addressing ergonomic conditions and physical workload in teaching environments is essential to promoting teacher health and sustaining job satisfaction.
Number of students in classrooms
One finding that initially appeared counterintuitive was that teachers with more than 50 students per class reported better work-life balance than those with smaller classes. This finding, however, may be explained by several contextual factors specific to the Nigerian secondary school environment. First, larger class sizes in Nigerian public secondary schools are frequently a proxy for seniority: experienced, longer-serving teachers tend to be assigned to larger, often senior classes, and such teachers may have developed more effective coping strategies and routines that buffer against work-family conflict [9]. Second, in contexts of resource scarcity, larger enrolment can be associated with reduced individual assessment burden, as teachers may rely on standardised, group-based assessment methods rather than individualised feedback, thereby reducing per-teacher marking load [45]. Third, teachers in crowded classrooms may experience a sense of occupational purpose and professional identity that enhances motivation — a mechanism identified in the job demands-resources (JD-R) model [8] and consistent with findings on engagement and WLB [44]. It is important to note that this finding was unexpected, and the cross-sectional design does not permit causal inference. Future qualitative and longitudinal studies are needed to understand the mechanism through which larger class sizes may be associated with better perceived WLB among Nigerian teachers.
WLB dimensions: differential associations with work conditions
The dimension-level results in Table 4 reveal instructive patterns that extend beyond the composite WLB score. Teaching three or more subjects was associated with lower scores across all three dimensions — WIPL (mean difference: −2.4), PLIW (mean difference: −1.5), and WPLE (mean difference: −0.4) — indicating that excessive subject load not only intensifies interference of work with personal life but also undermines the positive enhancement that work might otherwise provide. Similarly, working eight or more hours daily was associated with poorer WIPL and PLIW scores, reflecting the direct intrusion of extended working hours into personal life domains. The negative association between prolonged standing (≥ 4 h) and PLIW (mean difference: −0.8; p < 0.001) is particularly noteworthy, highlighting physical fatigue as a source of personal life being unable to adequately support work performance — a dimension less frequently discussed in the literature but consistent with evidence on musculoskeletal strain and occupational fatigue among teachers [50,51]. The WPLE scores, while smaller in magnitude, showed statistically significant associations with standing time, suggesting that physical exhaustion from prolonged standing may also erode the positive mood and energy that personal life typically contributes to work. These dimension-level findings provide a more granular understanding of the pathways through which specific work conditions impair or sustain WLB.
Rural–Urban Differences in WLB
School location (rural versus urban) was not found to be a significant determinant of WLB in this study, contrary to the hypothesis that rural teachers would report poorer balance due to greater isolation, increased multi-subject teaching responsibilities, and limited support infrastructure. This null finding is itself noteworthy. It suggests that within Oyo State, structural work conditions — subject load, hours worked, and class size — are distributed in ways that produce comparable WLB outcomes across rural and urban schools. This may reflect the relative uniformity of the public school system under the Oyo State Universal Basic Education Board (SUBEB) where staffing norms and curriculum requirements are centrally administered. Alternatively, rural teachers may draw on stronger community and social support networks that buffer the effects of professional isolation, consistent with findings on rural teacher well-being in sub-Saharan Africa [34, 35]. The absence of rural-urban WLB differences reinforces the importance of investigating specific occupational exposures rather than relying on location as a proxy for work conditions.
Strengths and limitations
The main strength of this study is that we included teachers across rural and urban locations and public and private schools. This enhances its generalizability. A second strength is the large sample size of 1174 teachers. A limitation is that the study was cross-sectional design, which limits causal inference. In addition, self-reported data may be subject to information bias (recall or social desirability bias) [51] and the data was collected seven years ago; the situation may have changed over this period.
Conclusions
This cross-sectional study among 1,174 secondary school teachers in Southwest Nigeria demonstrates that work-life balance is significantly influenced by specific occupational work conditions. High subject load (teaching three or more subjects), long working hours (eight or more hours per day), and prolonged standing (four or more hours daily) were each independently associated with poorer WLB after adjustment for gender, age, marital status, and school type. These findings were consistent across all three WLB dimensions — work interference with personal life (WIPL), personal life interference with work (PLIW), and work/personal life enhancement (WPLE) — providing convergent evidence for the detrimental role of these occupational exposures. Notably, school location (rural vs. urban) was not a significant determinant of WLB, indicating that within Oyo State, structural work conditions produce comparable WLB outcomes across settings. This reinforces the importance of addressing specific occupational exposures rather than using location as a proxy. From a public health perspective, these findings highlight an urgent need for policy-level interventions in Nigerian secondary schools: workload redistribution through rational subject allocation policies and teacher deployment strategies; enforcement of reasonable working hour limits; and ergonomic improvements in classroom infrastructure to reduce physical fatigue from prolonged standing. At the school level, reduced non-teaching administrative duties and enhanced institutional support could meaningfully improve WLB.
Within the Nigerian context, these recommendations translate into specific, actionable interventions at three levels. At the policy level, the Universal Basic Education Commission [UBEC] and State Universal Basic Education Boards [SUBEBs] should revise teacher deployment guidelines to cap subject allocation, in recognition of the significant negative impact of teaching three or more subjects on WLB documented in this study. At the institutional level, school principals and management committees should enforce daily working schedules not exceeding eight hours, consistent with the Nigerian Labour Act, and rationalise non-teaching administrative duties such as lesson note preparation — identified as the primary occupational stressor by 34.4% of respondents in this study. At the school infrastructure level, education authorities should prioritise the provision of ergonomic teaching furniture, such as sit-stand desks and supportive chairs, in public secondary schools to address the documented health consequences of prolonged sitting among this occupational group. Public secondary schools, which constitute 71.3% of the sampled institutions, are particularly in need of infrastructural support to protect the well-being of teachers as a critical human resource within Nigeria’s educational system.
Future research should investigate the mechanisms underlying the unexpected association between larger class sizes and better WLB, and conduct multi-state studies to determine generalisability across Nigeria.
Supplementary Information
Acknowledgements
The authors would like to express our gratitude to all secondary school teachers who participated in the study. We also thank all those who assisted with data collection.
Abbreviations
- CI
Confidence Intervals
- DAG
Directed Acyclic Graph
- LGAs
Local Government Areas
- PLIW
Personal Life Interference with Work
- SPSS
Statistical Package for the Social Sciences
- WIPL
Work Interference with Personal Life
- WLB
Work–life balance
- WPLE
Work/Personal Life Enhancement
Authors’ contributions
MB, FO and OP conceptualized and designed the study. CD did data curation, analysis and interpretation. MB and CD wrote the manuscript. All the authors were involved in reviewing and editing the manuscript **.**.
Funding
The study received funding from ExxonMobil.
Data availability
The dataset supporting the conclusions of this article is available upon request.
Declarations
Ethics approval and consent to participate
Ethical approval was obtained from the Oyo State Ministry of Health Ethical Review Committee. (Ethics approval number AD13/479/437).The study was conducted in line with the National Code for Health Research Ethics in Nigeria. Verbal and written informed consent were obtained from all participants after the purpose of the study was explained to them.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The dataset supporting the conclusions of this article is available upon request.