Safe spaces enhancing sexual and reproductive health for youth: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Safe spaces play a crucial role in providing support for the sexual and reproductive health (SRH) of youth. As young individuals undergo significant physical and emotional changes, they often encounter challenges such as societal taboos and a lack of accessible information related to their SRH needs. This scoping review explores the existing literature on using safe spaces to offer a supportive environment for adolescents SRH to navigate these complexities. METHODS: This review adhered to Arksey and O'Malley's scoping review framework method. A comprehensive search was conducted across Medline, EMBASE, CINAHL, and Scopus for studies published between January 2013 and December 2023. It focused on youth aged 15 to < 25 years, including primary studies and grey literature in English from diverse global contexts, excluding non-English studies and scoping/systematic reviews. The screening was done using Covidence software by two independent reviewers. Data were extracted and analyzed using descriptive statistics and narrative synthesis to summarize the findings. RESULTS: Schools emerged as the most common safe spaces (37 studies), effectively increasing condom use, SRH knowledge, and service utilization. Community-based initiatives (e.g., youth clubs and outreach programs) were critical in reducing stigma and fostering positive SRH attitudes. At the same time, digital platforms demonstrated the potential to address high-risk behaviours and unplanned pregnancies. Interventions included educational sessions, resource distribution, counseling, and peer support. While most studies reported positive outcomes, the evidence for long-term efficacy and sustainability was limited. CONCLUSION: Safe spaces hold significant potential to improve youth SRH outcomes by fostering informed decision-making and reducing risky behaviors. However, the effectiveness of interventions should be critically evaluated, with more emphasis on innovative, digital approaches and long-term impact. Tailored, inclusive, and sustainable strategies are essential to address the diverse needs of youth globally.
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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".