Obstetric competence among referral healthcare providers in Mali
Bibliographic record
Abstract
OBJECTIVE: To determine the factors associated with obstetric competency and clinical practice among obstetric care providers in referral health centers in Mali. METHODS: The present cross-sectional study was conducted between March and May 2012 among 140 obstetric care providers (obstetric nurses, midwives, and physicians) working in referral health centers in Mali. Emergency obstetric care knowledge and skills were evaluated with clinical vignettes developed using national Malian guidelines. The vignettes covered 5 areas of emergency obstetric care, and the results were used to generate a competency score. A backward stepwise random-effects model using a maximum likelihood estimator was applied to evaluate variables independently associated with competency score. RESULTS: Out of 100, the mean±SD score was 57.8±11.2 for obstetric nurses, 66.4±14.7 for midwifes, and 78.6±13.4 for physicians (P<0.001). Three variables were significantly associated with a higher competency score: professional qualification, working in an urban setting, and working in a health center with a smaller number of obstetric care providers. CONCLUSION: Increasing the in-service training of both rural staff and lower-level healthcare workers working in larger health centers via facility-based maternal death reviews might help to improve clinical practice and maternal health outcomes.
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".