Impact of the COVID-19 Pandemic on Adolescents’ Sexual and Reproductive Health in Low- and Middle-Income Countries
Bibliographic record
Abstract
Adolescents living in low- and middle-income countries (LMICs) are struggling with accessing sexual and reproductive health (SRH) services, and COVID-19 has escalated the problem. The purpose of this review was to identify and assess the existing literature on the impact of the pandemic on SRH needs and access to services by adolescents in LMICs. A scoping review was conducted to collate findings on the topic. Searches were performed on eight databases. Data were extracted and categorized into various themes. After removing duplicates and performing a full-text reading of all articles, nine articles were included in our review. Our findings generated several themes related to adolescents' sexual and reproductive health during the COVID-19 pandemic. These include (1) limited access to sexual and reproductive health services, (2) school closure and increased rate of early marriages, (3) sexual or intimate partner violence during COVID-19, (4) disruption in maternity care, (5) adolescents' involvement in risky or exploitative work, (6) intervention to improve sexual and reproductive health services during COVID-19, and (7) policy development related to adolescent sexual and reproductive health. Several recommendations were made on policies-for instance, the use of telemedicine and community-based programs as a way to deliver SRH services to adolescents during and after a pandemic.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".