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Record W2044870449 · doi:10.1300/j069v19n02_04

Identifying Office Resource Needs of Canadian Physicians to Help Prevent, Assess and Treat Patients with Substance Use and Pathological Gambling Disorders

2000· article· en· W2044870449 on OpenAlexaffabout
Margo Rowan, Colleen S. Galasso

Bibliographic record

VenueJournal of Addictive Diseases · 2000
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCanadian Medical AssociationDalhousie University
Fundersnot available
KeywordsSubstance useMedicinePathologicalFamily medicineFocus groupPsychiatryBusinessPathology

Abstract

fetched live from OpenAlex

A study was conducted to determine whether there is a need for office-based resources to assist physicians in the prevention, assessment and/or management of patients, self and peers with substance use (excluding alcohol and tobacco) or pathological gambling disorders. The needs assessment was undertaken in three parts. Survey information was collected from 54 respondents including Executive Directors of the Canadian Medical Association's national affiliates and provincial/territorial Divisions, Deputy Ministers of Health, Registrars of provincial/territorial licensing bodies and Deans or Associate Deans of Continuing Medical Education programs in universities. Key informant interviews were conducted with 22 "experts" in the field of substance use and/or pathological gambling disorders. Focus groups were held in Vancouver, Toronto, Ottawa, Montreal and Halifax with 34 physicians who are interested in and whose caseload included patients with substance use and/or gambling disorders. Results suggest that current resources for both substance use and pathological gambling disorders are inadequate for physicians because there are gaps in the types activities and resources available, existing resources have not been effectively diffused or disseminated to physicians and training is needed to complement these resources to encourage proper implementation.

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)0.000
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 score0.018
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.060
GPT teacher head0.320
Teacher spread0.259 · 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

Citations23
Published2000
Admission routes2
Has abstractyes

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