Designing an intervention to improve sexual health service use among university undergraduate students: a mixed methods study guided by the behaviour change wheel
Bibliographic record
Abstract
INTRODUCTION: University undergraduate students are within the population at highest risk for acquiring sexually transmitted infections, unplanned pregnancy, and other negative health outcomes. Despite the availability of sexual health services at university health centres, many students delay or avoid seeking care. In this study, we describe how the Behaviour Change Wheel was used as a systematic approach to design an intervention to improve sexual health service use among university undergraduate students. METHODS: This paper describes the intervention development phase of a three-phased, sequential explanatory mixed methods study. Phases one and two included a quantitative and qualitative study that aimed to better understand students' use of sexual health services. In phase three, we followed the Behaviour Change Wheel to integrate the quantitative and qualitative findings and conduct stakeholder consultation meetings to select intervention strategies, including intervention functions and behaviour change techniques. RESULTS: Key linkages between opportunity and motivation were found to influence students' access of sexual health services. Stakeholders identified six intervention functions (education, environmental restructuring, enablement, modelling, persuasion, and incentivization) and 15 behaviour change techniques (information about health consequences, information about social and environmental consequences, feedback on behaviour, feedback on outcomes of behaviour, prompts/cues, self-monitoring of behaviour, adding objects to the environment, goal setting, problem solving, action planning, restructuring the social environment, restructuring the physical environment, demonstration of the behaviour, social support, credible source) as relevant to include in a toolbox of intervention strategies to improve sexual health service use. CONCLUSIONS: This study details the use of the Behaviour Change Wheel to develop an intervention aimed at improving university students' use of sexual health services. The Behaviour Change Wheel provided a comprehensive framework for integrating multiple sources of data to inform the selection of intervention strategies. Stakeholders can use these strategies to design and implement sexual health service interventions that are feasible within the context of their health centre. Future research is needed to test the effectiveness of the strategies at changing university students' sexual health behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".