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Record W2284390688 · doi:10.1080/14459795.2016.1139159

What mental health professionals in Israel know and think about adolescent problem gambling

2016· article· en· W2284390688 on OpenAlexaff
Rayna M. Sansanwal, Jeffrey L. Derevensky, Belle Gavriel‐Fried

Bibliographic record

VenueInternational Gambling Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsMental healthPsychologyHealth professionalsPsychiatryNeed to knowSocial psychologyApplied psychologyHealth carePolitical scienceComputer scienceComputer securityLaw

Abstract

fetched live from OpenAlex

Mental health professionals are well versed in addressing multiple adolescent risky behaviours and play a primary role in the identification of and referral process and service provision for young people who engage in such behaviours. Given their ‘person-in-environment’ approach, training in multi-sectoral collaboration, and awareness of social policies, social workers are especially equipped to provide needed mental health services to young people. The aim of the current study was to examine Israeli mental health professionals’ awareness of and attitudes towards adolescent high-risk behaviours, including gambling. Child psychologists, social workers and school counsellors (N = 273) completed an online survey addressing concerns related to high-risk behaviours. Findings revealed that social workers perceived gambling as being among one of the least concerning adolescent mental health issues and reported feeling the least confident in their abilities to provide services to young people with gambling problems. The results suggest the importance of youth gambling addictions being incorporated into social work training curricula.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.133
GPT teacher head0.484
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations54
Published2016
Admission routes1
Has abstractyes

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