A matched pair cluster randomized implementation trail to measure the effectiveness of an intervention package aiming to decrease perinatal mortality and increase institution-based obstetric care among indigenous women in Guatemala: study protocol
Bibliographic record
Abstract
BACKGROUND: Maternal and perinatal mortality continue to be a high priority problem on the health agendas of less developed countries. Despite the progress made in the last decade to quantify the magnitude of maternal mortality, few interventions have been implemented with the intent to measure impact directly on maternal or perinatal deaths. The success of interventions implemented in less developed countries to reduce mortality has been questioned, in terms of the tendency to maintain a clinical perspective with a focus on purely medical care separate from community-based approaches that take cultural and social aspects of maternal and perinatal deaths into account. Our innovative approach utilizes both the clinical and community perspectives; moreover, our study will report the weight that each of these components may have had on reducing perinatal mortality and increasing institution-based deliveries. METHODS/DESIGN: A matched pair cluster-randomized trial will be conducted in clinics in four rural indigenous districts with the highest maternal mortality ratios in Guatemala. The individual clinic will serve as the unit of randomization, with 15 matched pairs of control and intervention clinics composing the final sample. Three interventions will be implemented in indigenous, rural and poor populations: a simulation training program for emergency obstetric and perinatal care, increased participation of the professional midwife in strengthening the link between traditional birth attendants (TBA) and the formal health care system, and a social marketing campaign to promote institution-based deliveries. No external intervention is planned for control clinics, although enhanced monitoring, surveillance and data collection will occur throughout the study in all clinics throughout the four districts. All obstetric events occurring in any of the participating health facilities and districts during the 18 months implementation period will be included in the analysis, controlling for the cluster design. Our main outcome measures will be the change in perinatal mortality and in the proportion of institution-based deliveries. DISCUSSION: A unique feature of this protocol is that we are not proposing an individual intervention, but rather a package of interventions, which is designed to address the complexities and realities of maternal and perinatal mortality in developing countries. To date, many other countries, has focused its efforts to decrease maternal mortality indirectly by improving infrastructure and data collection systems rather than on implementing specific interventions to directly improve outcomes. TRIAL REGISTRATION: ClinicalTrial.gov,http://NCT01653626.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".