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Record W7133013575

Assessing the Emotional and Behavioural Needs of Youth in Out-of-School Programming: A Scoping Review

2023· dissertation· W7133013575 on OpenAlexaff
Sabrina Brodkin

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsMental healthContext (archaeology)Thematic analysisSocial emotional learningNeeds assessmentPositive Youth Development
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.155
GPT teacher head0.463
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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