Barriers to Sexual and Reproductive Health Information among University Students in Uganda: A qualitative study
Bibliographic record
Abstract
Abstract Background: University students are one of the most vulnerable groups to sexual and reproductive health (SRH) threats, yet they often have limited access to SRH tools, services and information. This study explored university students’ perceptions of SRH, their source of SRH information and how they authenticated such information in Uganda. Methods: Data were collected from 40 students of Kyambogo university—20 males and 20 females through 4 gender-classified Focus Group Discussions (FGDs). Atlas ti 8 software was used to analyse the data and generate themes from interview transcripts.Results: Many students perceived SRH to be about sexual intercourse and its related consequences. Perceived risky behaviours included mainly having multiple sexual partners. The students identified the reduced sexual sensitivity, struggle to bear children in the future, as the myths and misconceptions about different SRH goods and services in addition to the uncertainty over safe days. The various sources of SRH information included Google, social media, health centres, friends, parents and government and non-government organizations. The main barriers to accessing SRH information included lack of finances, inadequate or few medical personnel, and service provider bias. Many of them authenticated the SRH information from the internet through their friends. Conclusion: New approaches and interventions should target both students and their parents, as this multifaceted approach will reduce the societal stigma, bias, ignorance, negative attitudes, and exposure to risky sexual behaviours among such youth populations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".