Assessing outcomes and satisfaction in a sustained national primary care youth mental health programme: a brief multi-methods report
Bibliographic record
Abstract
Abstract Objectives: Most mental health difficulties have their onset in early adolescence. Increasingly, community based primary care is recognised as a critical pathway to early intervention. Despite encouraging initial evaluations, there is an ongoing need for evidence of the outcomes of primary care youth mental health programmes delivered at scale. This brief report examines reliable improvements in psychological distress and user satisfaction data from a national primary care youth mental health programme in the sustainment phase of implementation. Methods: This report takes a multi-methods approach to routine evaluation data. Young people (aged 12–25; N = 8,721) completed Clinical Outcomes Routine Evaluation (CORE-10 and YP-CORE) pre- and post-treatment. Clinical cut offs and a reliable change index (based on established guidelines) were used to report rates of reliable improvement. The analysis examined differences in outcomes based on age, gender, and clinical need. Satisfaction was measured using the youth service satisfaction survey ( N = 4,267). Natural language processing techniques were employed to objectively analyse qualitative user feedback. Results: Most young people presented in the clinical range, with almost two-thirds reporting moderate to severe distress. Statistically significant reductions in distress were observed with large effect sizes ( d = 1.08–1.28). Young people in the clinical range demonstrated significantly higher rates of reliable improvement compared to those who presented in the healthy range. In line with similar evaluations, young adults were more likely to achieve improvement and report higher satisfaction. Sentiment analysis of satisfaction data indicated a strong skew towards positive sentiment, with trust, anticipation and joy being predominant. Qualitative feedback pointed to waiting times as an improvement area. Conclusion: The absence of a control group limits our ability to evaluate the effectiveness of the service interventions. Nonetheless after a decade of service delivery, these results indicate that large scale national youth mental health programmes can achieve satisfaction and clinical outcomes in line with international standards. Further research is needed on the predictors of reliable change, differences across demographic groups and approaches to improving waiting times in primary care.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".