DECIDE: a cluster randomized controlled trial to reduce non-medically indicated caesareans in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Since 2006, Burkina Faso has subsidized the cost of caesarean sections to increase their accessibility. Caesareans are performed by obstetricians, general practitioners, and nurses trained in emergency surgery. While the national caesarean rate is still too low (only 2 % in 2010), 12 to 24 % of caesareans performed in hospital are, in fact, not medically indicated. The objective of this study is to evaluate the effectiveness and analyze the implementation of a multi-faceted intervention to lower the rate of non-medically indicated caesareans in Burkina Faso. METHODS: This study combines a multicentre cluster randomized controlled trial with an implementation analysis in a mixed-methods approach. The evidence-based intervention will consist of three strategies to improve the competencies of maternity teams: 1) clinical audits based on objective criteria; 2) training of personnel; and 3) decision-support reminders of indications for caesareans via text messages. The unit of randomization and of intervention is the public hospital equipped with a functional operating room. Using stratified randomization on hospital type and staff qualifications, 11 hospitals have been assigned to the intervention group and 11 to the control group. The intervention will cover 1 year. Every patient who delivered by caesarean during a 6-month period in the year preceding the intervention and the 6 months following its end will be included in the trial. The change in the rate of non-medically indicated caesareans is the main criterion by which the intervention's impact will be assessed. To analyze the intervention process, a longitudinal qualitative study consisting of deliberative workshops and individual in-depth interviews will be conducted. The target outcome is a 50 % reduction in the rate of non-medically indicated caesareans. DISCUSSION: This study will provide evidence regarding the effectiveness of a multi-faceted intervention for reducing non-medically indicated caesareans in a low-income country. By combining qualitative and quantitative methods, the study's findings will allow understanding the factors that could influence the intervention process and ultimately the intended outcomes. TRIAL REGISTRATION: The DECIDE trial is registered on the Current Controlled Trials website under the number ISRCTN48510263 on January 28, 2014.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".