Investigating Project Care UK, a Web-Based Self-Help Single-Session Intervention for Youth Mental Health: Program Evaluation
Bibliographic record
Abstract
BACKGROUND: Psychological distress becomes more common during adolescence, yet many young people struggle to access clinic-based mental health care. Digital, self-help single-session interventions (SSIs) could extend current provision and overcome barriers to help seeking. OBJECTIVE: This study aims to pilot Project Care UK, a self-compassion-focused SSI, to examine its feasibility, acceptability, and preliminary efficacy for UK adolescents aged between 13 and 18 years. METHODS: We used a single-arm, within-subjects pre-post intervention program evaluation. Consenting participants completed a demographic survey and clinical measures at baseline. Self-assessments of hope, hopelessness, negative beliefs about self-compassion, and help seeking were measured immediately before and after the intervention. Acceptability and feasibility were measured after the intervention using the Program Feedback Scale and study completion metrics. Preliminary efficacy was evaluated using linear mixed-effects models. The study protocol was preregistered on the Open Science Framework before publication. RESULTS: Of the 813 individuals who gave consent for the study, 714 (87.8%) initiated the preintervention assessment survey, 610 (75%) initiated the intervention, 341 (41.9%) initiated the Program Feedback Scale, and 329 (40.5%) initiated the postintervention assessment survey. The sample consisted of adolescents (mean age 15.38, SD 1.58 y) who were predominantly assigned female sex at birth, were White, and were nonheterosexual. Intervention completers widely endorsed the intervention as acceptable. Significant, favorable pre- and postintervention changes were observed across all outcome measures, including increased hope (Cohen d=0.72, P<.001), decreased hopelessness (Cohen d=-0.73, P<.001), and reduced negative beliefs about self-compassion (Cohen d=-0.64, P<.001). No significant changes were observed for help-seeking intentions. CONCLUSIONS: Although not all participants completed the study, our findings show that recruiting adolescents in the United Kingdom is feasible; completers indicated that the intervention was acceptable, and they showed improvements in the proximal outcomes of hope, hopelessness, and beliefs about self-compassion. More extensive follow-up over time and comparator intervention analyses would allow more robust conclusions to be drawn.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".