Fertility Awareness-Based Methods for Family Planning: A Systematic Review
Bibliographic record
Abstract
Fertility awareness-based methods (FABMs) have been used for a long time for family planning. This systematic review evaluates the efficacy and outcomes of various FABMs used for family planning. For this systematic review, a literature search was carried out in PubMed, Web of Science, CINAHL Ultimate, and Google Scholar. The inclusion criteria included women aged 18-49 undergoing FABMs for either contraception or to conceive. The search was limited from 2014 to 2024. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) and the Revised Cochrane risk of bias tool for randomized trials (ROB2). A total of 16 studies, including 20,339 participants, were included. The age of the participants ranged from 18 to 47 years. Regarding study design, 11 were prospective, two were retrospective, two were randomized, and one was a longitudinal study. The average success rate of all FABMs was 69.5%. In five studies, the success rate was above 90%. Among factors that influenced the success rate were the timing of intercourse and adherence to method protocols. FABMs are effective tools for enhancing the success rate of family planning. However, FABMs when enhanced with digital technology are particularly effective for both contraception and conception. Adequate user education and consistent application are essential to optimize outcomes.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".