Digital Knowledge Translation Tools for Disseminating Sexual and Reproductive Health Information to Adolescents: Protocol for an Evidence Gap Map Review
Bibliographic record
Abstract
BACKGROUND: Digital or eHealth knowledge translation (KT) interventions have been identified as useful public health tools, particularly to advance sexual and reproductive health (SRH) among adolescents. Existing literature reviews on digital health interventions for adolescents' SRH demonstrate limitations, including shortcomings in reporting and comprehensiveness that limit the utility and trustworthiness of findings. However, there is a lack of evidence synthesis on the effectiveness of available digital or mobile health KT tools to promote SRH interventions for adolescents. OBJECTIVE: We aim to identify, map, and describe existing empirical evidence on the digital KT tools developed to improve adolescent SRH outcomes globally. METHODS: This study will be conducted using an evidence gap map (EGM) approach to address the objectives, including reviewing relevant literature and a landscape analysis of the outcomes of interest. The following electronic databases will be searched for retrieval of literature: MEDLINE (1946-present), Embase (1974-present), and Global Health (1910-present) via OVID; CINAHL (1936-present) via EBSCOhost; Scopus (1976-present); and Cochrane Library (1993-present) via Wiley. We will include only those studies that focused on adolescents aged 10-19 years and addressed SRH outcomes. We will include experimental studies (randomized or cluster randomized and nonrandomized controlled trials, including quasi-randomized, controlled before-after, and interruptive time series) and observational studies, that is, including prospective cohort and case-control studies. The experimental and observational studies will only be included in the presence of control or comparison arms. Studies with a historical control arm will be excluded. The systematic review software, Covidence (Ventas Health Innovation), will be used to screen and select the studies. Further, 2 independent reviewers will complete the first and second levels of screening of studies and any conflicts arising will be resolved by consensus between the 2 reviewers or by involving the third reviewer. We will conduct the quality assessment of all included studies using the Risk of Bias tool for randomized controlled trials and nonrandomized controlled trials, and AMSTAR2 for systematic reviews. RESULTS: Papers screening, data extraction, and synthesis will be completed by March 2024. We will use EPPI-Mapper (The International Public Policy Observatory) software to generate an online evidence map and to produce the tables and figures for the descriptive report. This EGM review will identify areas with high-quality, evidence-based digital KT tools (for immediate scale and spread) and areas where few or no KT tools exist (for targeted KT tool development and research or policy prioritization). CONCLUSIONS: This protocol focused on mapping eHealth KT tools that have been used in the literature to address SRH among adolescents. This will be the first EGM exercise to map digital KT tools to promote adolescents' SRH and will incorporate a range of published sources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55081.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.087 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.106 | 0.012 |
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".