Exploring Access, Barriers, and Opportunities in Digital Health to Improve Sexual and Reproductive Health amongst Youths in Bamenda, Cameroon during Conflict: A Qualitative Study
Bibliographic record
Abstract
Abstract Background:There exists several unmet sexual and reproductive health needs in sub-Saharan African countries like Cameroon. Youths in Bamenda, Northwest Region of Cameroon face particular difficulties accessing sexual and reproductive health services fully. This is partly due to the ongoing socio-economic crisis in the region which has disrupted provision of health care services, but also due to other issues that youths must contend with to access sexual and reproductive healthcare. Methods: To explore this topic, we conducted a qualitative study. Two focus group discussions were done in Bamenda: One with boys and another with girls aged between 15-24 years old. Questions focused on access and use of sexual and reproductive health services like contraception, pregnancy, menstrual hygiene, sexually transmitted diseases, HIV, and gender-based violence. A thematic data analysis grouped data into themes and sub-themes. Results: Twelve youth leaders participated in the focus group discussions: Three boys and nine girls. In this study, we found that several sexual and reproductive health services are available to youths. However, their ability to fully access these services is compromised by certain fundamental barriers like stigma and equally the socio-political crisis. Conclusion: There are several barriers to sexual and reproductive services amongst youths in the conflict-affected town of Bamenda. Digital health can significantly bridge the gap to improve access to sexual and reproductive health.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".