Pattern and perception of wellbeing, quality of work life and quality of care of health professionals in Southwest Nigeria
Bibliographic record
Abstract
BACKGROUND: Personal wellbeing (PW) including quality of life and work life is a very complex concept that influences health professionals' commitment and productivity. Improving PW may result in positive outcomes and good quality of care. Therefore, this study aimed to assess the pattern and perception of wellbeing, quality of work life (QoWL) and quality of care (QoC) of health professionals (HPs) in southwest Nigeria. METHODS: The study was a convergent parallel mixed method design comprising a cross-sectional survey (1580 conveniently selected participants) and a focus group interview (40 purposively selected participants). Participants' PW, quality of life (QoL), QoWL, and QoC were assessed using the PW Index Scale, 5-item World Health Organization Well-Being Index, QoWL questionnaire, and Clinician QoC scale, respectively. The pattern of wellbeing, QoWL and quality of care of HPs were evaluated using t-test and ANOVA tests. Binary regression analysis was used to assess factors that could classify participants as having good or poor wellbeing, QoWL, and quality of care of HPs. The qualitative findings were thematically analyzed following two independent transcriptions. An inductive approach to naming themes was used. Codes were assigned to the data and common codes were grouped into categories, leading to themes and subthemes. RESULTS: Of 1600 administered questionnaires, 1580 were returned, giving a 98.75% response rate. Only 45.3%, 43.9%, 39.8% and 38.4% of HP reported good PW, QoL, QoC and QoWL, respectively; while 54.7%, 56.1%, 60.2% and 61.6% were poor. There were significant gender differences in PW and QoC in favor of females. With an increase in age and years of practice, there was a significant increase in PW, QoWL and QoC. As the work volume increased, there was significant decrease in QoWL. Participants with master's or Ph.D. degrees reported improved QoWL while those with diploma reported better QoC. PWI and QoC were significantly different along the type of appointment, with those who held part-time appointments having the least values. The regression models showed that participant's characteristics such as age, gender, designation, and work volume significantly classified health professionals who had good or poor QoC, QoWL, PW and QoL. The focus group interview revealed four themes and 16 sub-themes. The four themes were the definitions of QoC, QoWL, and PW, and dimensions of QoC. CONCLUSION: More than half of health professionals reported poor quality of work life, quality of life and personal wellbeing which were influenced by personal and work-related factors. All these may have influenced the poor quality of care reported, despite the finding of a good knowledge of what quality of care entails.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".