Preconception health care interventions: A scoping review
Bibliographic record
Abstract
Pregnancy is often framed as a "window of opportunity" for intervening on a variety of health practices such as alcohol and tobacco use. However, there is evidence that interventions focusing solely on the time of pregnancy can be too narrow and potentially stigmatizing. Indeed, health risks observed in the preconception period often continue during pregnancy. Using a scoping review methodology, this study consolidates knowledge and information related to current preconception and interconception health care interventions published in the academic literature. We identified a total of 29 intervention evaluations, and summarized these narratively. Findings suggest that there has been some progress in intervening on preconception health, with the majority of interventions offering assessment or screening followed by brief intervention or counselling. Overall, these interventions demonstrated improvements in at least some of the outcomes measured. However, further preconception care research and intervention design is needed. In particular, the integration of gender transformative principles into preconception care is needed, along with further intervention design for partners/ men, and more investigation on how best to deliver preconception care.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".