Assessment of job satisfaction and retention among medical doctors and nurses in a developing country setting: a survey in Calabar, Nigeria
Bibliographic record
Abstract
Background: Globally, there is a rising shortage of healthcare workers, especially among doctors and nurses, potentially impairing quality of care provision. In Nigeria, this deficient workforce is further worsened by job dissatisfaction and brain drain. This study was aimed at assessing the determinants of job satisfaction and retention among medical doctors and nurses in public hospitals. Methods: Cross-sectional study design and systematic random sampling was conducted among medical doctors and nurses working in the two main public hospitals in Calabar Southern Nigeria. Data was entered and analyzed using statistical package for the social sciences (SPSS) version 25. Chi-square and independent t-test were employed as inferential statistics, with p value set at 0.05. Results: Two hundred and eighty-seven (287) subjects were studied. The mean age was 36.12±7.7 years ranging from 22 to 59 years. The overall level of job dissatisfaction was 49.5%, comprising 60.2% and 42.0% for doctors and nurses, respectively. Approximately a quarter of respondents (23.3%) were considered to be high retention risk. Younger age, male gender, having less than 10 years of employment duration, suboptimal remunerations and poor occupational environment, were associated with job dissatisfaction and higher retention risk (p<0.05). Conclusions: Approximately half of medical doctors and nurses are dissatisfied with their job, with attendant high retention risk. Improved funding of the public health sector is recommended, towards containing this challenge, within the context of worsening brain drain of skilled workers from developing to more developed countries. Further studies among private sector, non-urban settings and lower level of health facilities is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".