Skill Enactment Among University Students Using a Brief Video-Based Mental Health Intervention: Mixed Methods Study Within a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mental health problems are common among university students, yet many students do not seek professional help. Digital mental health interventions can increase students' access to support and have been shown to be effective in preventing and treating mental health problems. However, little is known about the extent to which students implement therapeutic skills from these programs in everyday life (ie, skill enactment) or about the impact of skill enactment on outcomes. OBJECTIVE: This study aims to assess the effects of a low-intensity video-based intervention, Uni Virtual Clinic Lite (UVC-Lite), in improving skill enactment relative to an attention-control program (primary aim) and examine whether skill enactment influences symptoms of depression and anxiety (secondary aim). The study also qualitatively explored participants' experiences of, and motivations for, engaging with the therapeutic techniques. METHODS: We analyzed data from a randomized controlled trial testing the effectiveness of UVC-Lite for symptoms of depression and anxiety among university students with mild to moderate levels of psychological distress. Participants were recruited from universities across Australia and randomly assigned to 6 weeks of self-guided use of UVC-Lite (243/487, 49.9%) or an attention-control program (244/487, 50.1%). Quantitative data on skill enactment, depression, and anxiety were collected through baseline, postintervention, and 3- and 6-month follow-up surveys. Qualitative data were obtained from 29 intervention-group participants through open-ended questions during postintervention surveys (n=17, 59%) and semistructured interviews (n=12, 41%) after the intervention period concluded. RESULTS: =73.08; P<.001). Inductive content analysis confirmed low levels of skill enactment among intervention group participants. Participants were motivated to use techniques and skills that were perceived to be personally relevant, easily integrated into daily life, and that were novel or had worked for them in the past. CONCLUSIONS: The intervention did not improve skill enactment or mental health among students with mild to moderate psychological distress. Low adherence impacted our ability to draw robust conclusions regarding the intervention's impact on outcomes. Factors influencing skill enactment differed across individuals, suggesting that it may be necessary to tailor therapeutic skills and engagement strategies to the individual user. Theoretically informed research involving collaboration with end users is needed to understand the processes underlying skill enactment in digital mental health interventions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000375853; https://tinyurl.com/7b9ar54r.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".