Developing and validating a knowledge and skills test for Tanzanian birth attendants trained in PartoMa safe childbirth care
Bibliographic record
Abstract
Abstract Introduction Improving quality of maternity care in low- and middle-income countries is essential for reducing maternal and perinatal mortality and morbidity. Low dose-high frequency in-house training in routine and emergency maternity care is suggested to be central for this. To ensure the effectiveness and resource-efficiency of such training, a knowledge and skills test requiring minimal time and resources is needed. Therefore, we set out to develop and validate a test for routine use in busy, low-resource maternity units; to efficiently and effectively assess potential knowledge and skills gains over time when attending ‘low-dose, high-frequency’ training in integrated care during childbirth. Materials and Methods Using Messick’s standards, we developed a comprehensive yet time-efficient test covering childbirth surveillance, respectful care, and management of maternity and neonatal complications. Both expert and participant feedback informed the test design. The test was applied during a Tanzanian in-house training intervention (PartoMa) to assess its performance. Reliability was determined by Cronbach’s Alpha, while validity was evaluated through face and content validity, factor analysis, and Rasch analysis. Results After multiple revisions, Cronbach’s Alpha was below 0.7, indicating limited reliability. Experts agreed the test was well-designed for the intended content, with no concerns about clarity or relevance. Content validity was confirmed through expert judgment, reflecting the test’s goals. In pilot testing, 160 (84.6%) participants rated it excellent in achieving the study objectives. Exploratory factor analysis showed the test did not measure a single latent trait, and Rasch analysis revealed discrepancies between observed and expected responses on item scores. Conclusion The knowledge and skills test has shown promise for assessing healthcare providers in high-pressure, resource-limited settings, but its low Cronbach’s Alpha and limitations in test results highlight the need for refinement.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".