Morbidity and Mortality Linked to Unsafe Abortions in Cameroon—Difficulties in Accessing Safe Abortions: Systematic Review and Meta-Analysis. A Study Proposal
Bibliographic record
Abstract
Unsafe abortions constitute a public health problem. It is one of the causes of maternal mortality in the world and particularly in developing countries. Despite the progress made, maternal mortality remains high in Cameroon. The scarcity and disparity of data on abortions lead to a lack of strong evidence to advocate to decision-makers on the extent of the problems associated with abortions in Cameroon. Our objective is to estimate the rates of mortalities and complications related to unsafe abortions, as well as the difficulties of accessing safe abortions in Cameroon. We will carry out a systematic and meta-analytical review in the biomedical databases MEDLINE (Pubmed), Google Scholar and African Journal Online (AJOL) concerning unsafe abortions and/or difficulties in accessing safe abortions in Cameroon, without date or language restriction. Gray literature will be also consulted. Two authors will simultaneously select the studies and data extraction will be done using a Google Form. Proportions will be estimated on a random-effect model. The I2 and Q statistics will be used to assess the extent of heterogeneity across the studies. The outcome of both the quantitative and qualitative parts of the study will be commented. Death and morbidity due to abortions can be prevented. A concerted multidisciplinary and multicentric action would be essential.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.026 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".