Combined Clinical Audits and Low-Dose, High-frequency, In-service Training of Health Care Providers and Community Health Workers to Improve Maternal and Newborn Health in Mali: Protocol for a Pragmatic Cluster Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Although most births in Mali occur in health facilities, a substantial number of newborns still die during delivery and within the first 7 days of life, mainly because of existing training deficiencies and the challenges of maintaining intrapartum and postpartum care skills. OBJECTIVE: This trial aims to assess the effectiveness and cost-effectiveness of an intervention combining clinical audits and low-dose, high-frequency (LDHF) in-service training of health care providers and community health workers to reduce perinatal mortality. METHODS: The study is a three-arm cluster randomized controlled trial in the Koulikoro region in Mali. The units of randomization are each of 84 primary care facilities. Each trial arm will include 28 facilities. The facilities in the first intervention arm will receive support in implementing mortality and morbidity audits, followed by one-day LDHF training biweekly, for 6 months. The health workers in the second intervention arm (28 facilities) will receive a refresher course in maternal neonatal and child health (MNCH) for 10 days in a classroom setting, in addition to mortality and morbidity audits and LDHF hands-on training for 6 months. The control arm, also with 28 facilities, will consist solely of the standard MNCH refresher training delivered in a classroom setting. The main outcomes are perinatal deaths in the intervention arms compared with those in the control arm. A final sample of approximately 600 deliveries per cluster was expected for a total of 30,000 newborns over 14 months. Data sources included both routine health records and follow-up household surveys of all women who recently gave birth in the study facility 7 days postdelivery. Data collection tools will capture perinatal deaths, complications, and adverse events, as well as the status of the newborn during the perinatal period. A full economic evaluation will be conducted to determine the incremental cost-effectiveness of each of the case-based focused LDHF hands-on training strategies in comparison to MNCH refresher training in a classroom setting. RESULTS: The trial is complete. The recruitment began on July 15, 2019, and data collection began on July 23, 2019, and was completed in November 2020. Data cleaning or analyses began at the time of submission of the protocol. CONCLUSIONS: The results will provide policy makers and practitioners with crucial information on the impact of different health care provider training modalities on maternal and newborn health outcomes and how to successfully implement these strategies in resource-limited settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT03656237; https://clinicaltrials.gov/ct2/show/NCT03656237. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28644.
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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 | 0.043 | 0.034 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.010 |
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".