Assessing the Emotional and Behavioural Needs of Youth in Out-of-School Programming: A Scoping Review
Bibliographic record
Abstract
Ensuring the mental well-being of children and youth is a significant challenge in our society, and many do not have access to care. Out-of-school programming is a potentially more accessible option, so providing such programs with the tools and skills needed to bolster mental health may have a large impact on youth wellbeing. Research has demonstrated that a feasible needs assessment is an important step in providing support for individuals experiencing mental health challenges, yet many out-of-school programs are not doing so. One barrier may be a lack of knowledge regarding the tools and methods that can be used in this context. The present scoping review was conducted to identify the tools and methods that have been used to determine the emotional and behavioural needs of youth in out-of-school programs and to synthesize information regarding the tools and the context in which these tools and methods have been used. 57 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 can be used by out-of-school program staff as a step to best meet the emotional and behavioural needs of youth attending their programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".