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Record W2954375272 · doi:10.4309/jgi.2019.42.7

Promoting cross-sector collaboration and input into care planning via an integrated problem gambling and mental health service

2019· article· en· W2954375272 on OpenAlexvenueno aff
Laura E McCartney, Vicky Northe, Susannah Gordon, Evan Symons, Rob Shields, Anthony Kennedy, Stuart Lee

Bibliographic record

VenueJournal of Gambling Issues · 2019
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialDual diagnosisFeelingMental healthContext (archaeology)Mental illnessPsychologyPsychiatryComorbidityPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

While problem gambling and mental illnesses are highly comorbid, there are few examples of integrated problem gambling and mental illness services. This has meant that it is unclear whether such services are needed, why they may be utilised, and how they operate to support clients impacted by the comorbidity and clinicians providing them care. This study reported on data collected via telephone questionnaire-assisted interviews of 20 clients and 19 referrers who had accessed one such Australian integrated problem gambling and mental illness program between July 2014 and June 2016. Data revealed that clients often were referred in the context of psychiatric or psychosocial crisis, or when clinicians encountered clients who were not making progress and wanted a second opinion about diagnosis and treatment. Improved management of illness symptoms or gambling behaviour were commonly reported benefits and a number of clients reported gaining a feeling of reassurance and hope following assessment due to gaining a deeper understanding of their issues and available treatment options. Access to dual specialist problem gambling and mental illness expertise may therefore enhance treatment planning, management during crises and cross-sector collaboration to enhance access to and the impact of care for people experiencing comorbidity.ResumeBien que le jeu problématique et les maladies mentales aient un taux élevé de comorbidité, il existe peu d’exemples de services intégrés pour le jeu et la maladie mentale. En d’autres termes, il n’est pas clair si de tels services sont nécessaires, à quelles fins ils peuvent être utilisés et la manière dont ils fonctionnent pour aider les clients touchés par cette comorbidité et les cliniciens qui leur fournissent des soins. La présente étude a rendu compte des données recueillies lors d’entretiens assistés par un questionnaire téléphonique menés auprès de 20 clients et de 19 répondants qui avaient eu accès à l’un des programmes australiens intégrés de lutte contre le jeu problématique et la maladie mentale entre juillet 2014 et juin 2016. Les données révèlent que les clients étaient souvent recommandés à d’autres services dans le contexte d’une crise psychiatrique ou psychosociale ou lorsque les cliniciens rencontraient des clients qui n’avaient pas fait de progrès et qui souhaitaient obtenir un deuxième avis sur le diagnostic et le traitement. Une gestion améliorée des symptômes de la maladie ou du comportement de jeu constituait des avantages souvent rapportés, et un certain nombre de clients ont déclaré avoir ressenti du réconfort et de l’espoir après une évaluation, en raison d’une meilleure compréhension de leurs problèmes et des options de traitement disponibles. L’accès à une double expertise en matière de jeu problématique et de maladie mentale peut donc améliorer la planification du traitement, la gestion de crise et la collaboration intersectorielle afin d’améliorer l’accès aux soins et l’incidence des soins pour les personnes souffrant de cette comorbidité.

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 score0.037
Threshold uncertainty score0.894

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 communication0.0000.001
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 head0.116
GPT teacher head0.457
Teacher spread0.341 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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