What treatment outcomes matter in adolescent depression? A Q-study of priority profiles among mental health practitioners in the UK and Chile
Bibliographic record
Abstract
Evidence-based and person-centred care requires the measurement of treatment outcomes that matter to youth and mental health practitioners. Priorities, however, may vary not just between but also within stakeholder groups. This study used Q-methodology to explore differences in outcome priorities among mental health practitioners from two countries in relation to youth depression. Practitioners from the United Kingdom (UK) (n = 27) and Chile (n = 15) sorted 35 outcome descriptions by importance and completed brief semi-structured interviews about their sorting rationale. By-person principal component analysis (PCA) served to identify distinct priority profiles within each country sample; second-order PCA examined whether these profiles could be further reduced into cross-cultural "super profiles". We identified three UK outcome priority profiles (Reduced symptoms and enhanced well-being; improved individual coping and self-management; improved family coping and support), and two Chilean profiles (Strengthened identity and enhanced insight; symptom reduction and self-management). These could be further reduced into two cross-cultural super profiles: one prioritized outcomes related to reduced depressive symptoms and enhanced well-being; the other prioritized outcomes related to improved resilience resources within youth and families. A practitioner focus on symptom reduction aligns with a long-standing focus on symptomatic change in youth depression treatment studies, and with recent measurement recommendations. Less data and guidance are available to those practitioners who prioritize resilience outcomes. To raise the chances that such practitioners will engage in evidence-based practice and measurement-based care, measurement guidance for a broader set of outcomes may be needed.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".