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Record W2477065252 · doi:10.4309/jgi.2016.33.5

Addressing the Needs of Problem Gamblers With Co-Morbid Issues: Policy and Service Delivery Approaches

2016· article· en· W2477065252 on OpenAlexvenueno aff
Kathya Martyres, Phil Townshend

Bibliographic record

VenueJournal of Gambling Issues · 2016
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipGovernment (linguistics)Mental healthService (business)BusinessWork (physics)Public relationsService delivery frameworkService providerAddictionMental health serviceService systemMarketingPsychologyPolitical sciencePsychiatryFinanceEngineering

Abstract

fetched live from OpenAlex

Most people with gambling problems have at least one co-occurring condition and many experience multiple co-occurring conditions simultaneously. In many Western jurisdictions, a specialist service response has developed, with separate agencies and workforces established to respond to gambling problems. Despite the number of co-occurring issues that occur alongside gambling, research is limited on the prevalence of problem gambling across some service systems, such as mental health and family service sectors. However, it is reasonable to conclude that significant numbers of people with gambling problems are currently engaged in other health and welfare service sectors. Partnership work with other service sectors is therefore vital to respond to the needs of these people. In Victoria, Australia, a partnership program was established in gambling help services to improve integration and co-ordination between gambling, alcohol and drug, family support, and mental health service sectors. From the experience acquired in developing the program, we seek in this article to outline the benefits and challenges of implementing a cross-sector approach in gambling treatment service systems and to recommend effective strategies to develop a cross-sector approach, including creating an authorising environment at the government policy level.

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 score0.160
Threshold uncertainty score0.466

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 head0.474
GPT teacher head0.455
Teacher spread0.019 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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