Barriers and enablers to sexual health service use among university students: a qualitative descriptive study using the Theoretical Domains Framework and COM-B model
Bibliographic record
Abstract
BACKGROUND: University students are within the age group at highest risk for acquiring sexually transmitted infections and other negative health outcomes. Despite the availability of sexual health services at university health centres to promote sexual health, many students delay or avoid seeking care. This study aimed to identify the perceived barriers and enablers to sexual health service use among university undergraduate students. METHODS: We used a qualitative descriptive design to conduct semi-structured focus groups and key informant interviews with university students, health care providers, and university administrators at two university health centres in Nova Scotia, Canada. The semi-structured focus group and interview guides were developed using the Theoretical Domains Framework and COM-B Model. Data were analyzed using a directed content analysis approach, followed by inductive thematic analysis. RESULTS: We conducted 6 focus groups with a total of 56 undergraduate students (aged 18-25) and 7 key informant interviews with clinicians and administrators. We identified 10 barriers and enablers to sexual health service use, under 7 TDF domains: knowledge; memory, attention and decision-making processes; social influences; environmental context and resources; beliefs about consequences; optimism; and emotion. Key linkages between students' social opportunity and motivation were found to influence students' access of sexual health services. CONCLUSIONS: We identified barriers and enablers related to students' capability, opportunity and motivation that influence sexual health service use. We will use these findings to design an intervention that targets the identified barriers and enablers to improve students' use of sexual health services, and ultimately, their overall health and well-being.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".