Job Satisfaction and Intention of Primary Healthcare Workers to Leave: A Cross-Sectional Study in a Local Government Area in Lagos, Nigeria
Bibliographic record
Abstract
The increased intention of healthcare workers to leave the health sector is one of the many negative impacts of job dissatisfaction and poor working conditions among healthcare workers in Nigeria. This study assessed the level of job satisfaction and the intention of leaving the country or medical practice among primary healthcare workers in Lagos, Nigeria. The study was a descriptive cross-sectional among 235 respondents, selected using a multistage sampling method. An adapted self-administered questionnaire from the Minnesota questionnaire short form and the Job Description Index (JDI) was used for data collection. Data were analyzed with Statistical Package for Social Sciences (SPSS) version 22.0. Descriptive statistics were performed while Chi-square was used to determine the association between categorical variables and the level of significance was set at p <0.05. About half (50.6%) of the healthcare workers were satisfied with their jobs. Highest score 37.00 (32-40) for job satisfaction was found in the domain of management process; while the lowest score 16.00 (13-20) was found in the salary domain. The majority of the healthcare workers 201(85.5%) had the intention of leaving Nigeria for a better opportunity abroad. Healthcare workers were satisfied with the management process but dissatisfied with pay. Targeted interventions to improve the morale of healthcare workers at the primary healthcare level is recommended.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".