Digital knowledge translation tools for sexual and reproductive health information to adolescents: an evidence gap-map
Bibliographic record
Abstract
Background: Digital knowledge translation (KT) interventions play a crucial role in advancing adolescent sexual and reproductive health (ASRH). Despite the extensive literature on their effectiveness, there's a lack of synthesized evidence on the efficacy of digital KT tools for adolescent ASRH globally. Objectives: This review aimed to systematically identify and map existing empirical evidence on digital KT tools targeting ASRH outcomes and identify research gaps. Design: The review employed an evidence gap-map (EGM) approach following 2020 PRISMA reporting guidelines. Data sources and methods: A comprehensive literature search was conducted across databases including Medline, EMBASE, Global Health, CINAHL, Scopus, and Cochrane. Covidence software was used for data management. EPPI-Mapper software was used to synthesize findings and develop a graphical EGM. Results: The EGM comprises 68 studies: 59 experimental and 9 systematic reviews, predominantly from African (19 studies) and American regions (22 studies), with limited research from the Eastern Mediterranean and South East Asian regions. It examines digital KT tools' influence on sexual and reproductive health (SRH) outcomes, identifying research gaps. Websites are extensively studied for their impact on adolescent behavior, knowledge, attitude, and self-efficacy, yet research on their effects on ASRH and health services access is limited. Similarly, mobile apps and short message service (SMS)/text messages impact various aspects of SRH outcomes, but research on their effects on health services utilization is insufficient. Interventions like digital pamphlets and gaming lack exploration in health service access. OTT media and social media need further investigation. Mass media, including radio, television, and podcasts, are largely unexplored in adolescent SRH outcomes. Topics such as menstrual hygiene, abortion, and sexual and intimate partner violence also lack research. Conclusion: The review underscores the dominance of certain KT tool interventions like SMS and websites. Despite advancements, research gaps persist in exploring diverse digital platforms on underrepresented outcomes globally. Future research should expand exploration across digital platforms and broaden the scope of outcome measures. Trial registration: The protocol is registered with PROSPERO (CRD42022373970).
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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 | 0.047 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".