Integrating the digital culture of youth in clinical mental health assessments: protocol for the codesign and pilot test of an interview method
Bibliographic record
Abstract
INTRODUCTION: Digital media practices have varied implications for the mental health of youth, notably as a function of sociocultural and environmental factors. However, there are limited tools available to guide the assessment of digital culture in clinical practice. This study will aim to design and pilot test an interview tool for the assessment of youth digital culture, as a companion to the Cultural Formulation Interview which broadly assesses cultural factors in mental healthcare. METHODS AND ANALYSIS: We will recruit youth aged 16-35 years and receiving mental healthcare in Montreal, Canada, to codesign (n=10) and evaluate (n=20) the interview tool. We will also recruit clinician participants (n=10) to provide feedback on the interview. The tool will be developed with codesign participants using the nominal group technique and subsequently tested with the evaluation participants. We will provide the evaluation participants and clinicians with a written summary of the interview and will assess their perspectives on the feasibility, acceptability and utility of the interview method through surveys and debriefing interviews. We will conduct reflexive thematic analysis of the interview transcripts and descriptive quantitative analyses of the feasibility, acceptability and utility scores. ETHICS AND DISSEMINATION: The study received ethical approval from the Research Ethics Board of the CIUSSS de l'Ouest-de-l'Île-de-Montréal (MP-18-2025-1164). The results will be interpreted in consultation with codesign participants and will be disseminated through peer-reviewed publications, workshops for clinicians and academic conferences.
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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 | 0.089 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".