Obstetric competence among primary healthcare workers in Mali
Bibliographic record
Abstract
OBJECTIVE: To determine individual and contextual factors associated with emergency obstetric and neonatal care (EmONC) competency among primary healthcare staff in Mali. METHODS: Between November 2011 and April 2012, a competency test was administered to 196 healthcare workers in 65 community health centers in Mali. The test was scored from 0 to 100, and differences among 5 areas of EmONC were assessed. A multilevel linear regression model was used to determine individual and contextual factors associated with score. RESULTS: The mean score was 66.7 (minimum, 15.9; maximum, 97.7). Knowledge was most deficient for postpartum infection and hypertensive complications. Type of health worker, years of experience, number of days absent, and availability of guidelines for management of obstetric complications within the health center were positively associated with test score (P<0.05). Availability of guidelines was associated with higher competency of physicians, health technicians, and obstetric nurses (P<0.001), and seemed to influence the competency of healthcare workers with fewer than 10 years of experience in particular. CONCLUSION: Guidelines must be developed that will facilitate standardization of the management of postpartum infection and other less common complications for which healthcare workers show low competence. Strategies to increase use of these guidelines will be necessary.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".