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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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.310
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.133
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 teacher head, 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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