Training and nutritional components of PMTCT programmes associated with improved intrapartum quality of care in Mali and Senegal
Bibliographic record
Abstract
OBJECTIVE: Scale-up of prevention of mother-to-child transmission (PMTCT) of HIV programmes in sub-Saharan Africa has stimulated interest to assess whether these programmes can indirectly affect other health priorities. This study assesses whether PMTCT programmes, or components of these programmes, are associated with better obstetrical quality of care and how PMTCT may reinforce existing maternal health programmes. DESIGN: Cross-sectional analysis of data from a cluster-randomized trial called QUARITE. SETTING: Mali and Senegal, West Africa. PARTICIPANTS: Thirty-one referral hospitals and 612 obstetrical patients. INTERVENTION: The exposure of interest was PMTCT measured with a scale containing 10 components describing different prongs of a hospital PMTCT programme. Other variables of interest included: presence of a quality of care improvement programme, hospital resources and patient demographic characteristics. MAIN OUTCOME MEASURE: Obstetrical quality of care measured through a validated chart abstraction tool. RESULTS: Of 45 points, the mean hospital PMTCT score was 26.1 (SD: 6.7). Total PMTCT score was not significantly associated with quality of care, but programme component scores were. After adjustment for known predictors of quality of care, staff training in PMTCT (P = 0.03) and complementary nutritional services (P = 0.03) were significantly associated with better quality obstetrical care. A point increase in scores for either of these components was associated with 40% greater odds of good obstetrical care. CONCLUSIONS: PMTCT training and nutritional components are significantly associated with better quality intrapartum care. Health professionals' training in maternal healthcare and PMTCT could be combined to improve the quality of obstetric care in the region.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".