Mental health needs assessment for youth in out of school programs: A scoping review
Bibliographic record
Abstract
Abstract Out‐of‐school programs are an accessible option to bolster the mental well‐being of youth, who may be at risk of developing further emotional and behavioural concerns. Developing a more nuanced understanding of the tools and methods available to understand youth mental health needs, in the context of out‐of‐school programs is needed to provide relevant services. However, many out‐of‐school programs do not include such an assessment. One barrier to doing so may be a lack of knowledge regarding the tools that can be used in this specialised context. The present scoping review was conducted to identify the tools that have been used to determine the emotional and behavioural needs of youth attending out‐of‐school programs and to synthesise information regarding the context in which these tools have been used. Fifty‐seven articles met the criteria for the review, and within these articles, 69 unique measures of emotional and behavioural needs were identified. The measures were sorted into six thematic categories (self‐concept, emotion and behaviour regulation, mood, general mental health, social skills and resilience) and relevant characteristics were described. The findings of the present review may be helpful to out‐of‐school programs as a step to best meet the needs of participating youth. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement .
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".