Uptake and provision of self-care interventions for sexual and reproductive health: findings from a global values and preferences survey
Bibliographic record
Abstract
Self-care interventions hold the potential to improve sexual and reproductive health (SRH) and well-being. Yet key knowledge gaps remain regarding how knowledge and uptake vary across different types of self-care interventions. There is also limited understanding of health workers’ confidence in promoting SRH self-care interventions, and how this may differ based on personal uptake experiences. To address these knowledge gaps, we conducted a web-based cross-sectional survey among health workers and laypersons from July to November 2018. We investigated the following information about SRH self-care interventions: knowledge and uptake; decisions for use; and associations between health workers’ uptake and providing prescriptions, referrals, and/or information for these interventions. Participants (n = 837) included laypersons (n = 477) and health workers (n = 360) from 112 countries, with most representation from the WHO European Region (29.2%), followed by the Americas (28.4%) and African (23.2%) Regions. We found great heterogeneity in knowledge and uptake by type of SRH self-care intervention. Some interventions, such as oral contraception, were widely known in comparison with interventions such as STI self-sampling. Across interventions, participants perceived benefits of privacy, convenience, and accessibility. While pharmacies and doctors were preferred access points, this varied by type of self-care intervention. Health workers with knowledge of the self-care intervention, and who had themselves used the self-care intervention, were significantly more likely to feel confident in, and to have provided information or referrals to, the same intervention. This finding signals that health workers can be better engaged in learning about self-care SRH interventions and thereby become resources for expanding access.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".