Utilization of the partograph in primary health care facilities in southwestern Nigeria.
Bibliographic record
Abstract
OBJECTIVE: To assess knowledge about the partograph and its utilization among maternity care providers in primary health care in southwestern Nigeria. METHOD: Two hundred and seventy-five maternity care providers comprising of 64 CHEWS (23.3%), 74 Auxiliary midwives (26.9%), 123 Nurses/midwives (44.7%) and 14 medical doctors (5.1%) were interviewed in primary health centres and private hospitals in three states in southwestern Nigeria using a multi-stage sampling strategy. Knowledge about the partograph and assessment of labour were assessed with an interviewer-administered questionnaire. RESULTS: About a quarter of respondents, 75 (27.3%) had received prior training on the partograph. Only 25 (9.1%) reported that the partograpgh was available in their labour wards. Knowledge about the partograph was poor; only 18 (16.0%) of all respondents correctly mentioned at least one component part of the partograph, 21 (7.6%) correctly explained function of the alert line and 30 (10.9%) correctly explained function of the action line. Prior training significantly influenced knowledge about the partograph (gamma2 = 49.2; p < 0.05). Knowledge about assessment of labour was also poor: less than 50% of all respondents knew the normal duration of labour and just about 50% understood assessment for progress of labour. CONCLUSION: The partograpgh is not utilized for labour management in Nigeria. Knowledge about partograph and assessment during labour is grossly deficient. Findings suggest poor quality intrapartum care. Effective interventions to improve labour supervision skills and partograph utilization are urgently required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".