Characteristics of medical doctors working in public healthcare institutions in a Southern Nigerian State
Bibliographic record
Abstract
OBJECTIVE: This study assessed the characteristics of medical doctors working in public healthcare institutions and examined differences in some of the characteristics by geographical (urban versus rural) location. METHODS: A cross-sectional study of doctors working in public healthcare institutions using data obtained from 3 centres in Bayelsa, Nigeria. RESULTS: Three-quarters (75.4%) of the 280 medical doctors were males. Most of the doctors (68.6%) were working at tertiary healthcare level, 16.1% at primary and 15.4% at secondary healthcare levels. In terms of their professional positions, there were more medical officers (34.5%) relative to the other cadres while 17.2% were consultants. When their places of practice were dichotomised into rural and urban settings, 88.2% were practising in urban settings. A higher proportion of the 69 female doctors were practising in urban settings compared to rural settings (26.7% versus9.1% respectively, P=0.027). There was a statistically significant relationship between residency status and place of practice (P=0.001). Specialists (i.e. doctors who have completed residency training) were more likely to practice in urban (19.2%) than in rural settings (3.3%). CONCLUSION: Only a quarter of doctors in this study were females. There seemed to be more doctors at tertiary level of care and in urban areas. These findings suggest that there may be a shortage of female doctors, and that there may be unmet personnel needs at primary and secondary healthcare levels and in rural areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".