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Record W2127068156 · doi:10.5539/gjhs.v4n3p13

Determinants of the Utilization of Diversified Types of Professionals for Mental Health Reasons in a Montreal (Canadian) Catchment Area

2012· article· en· W2127068156 on OpenAlexafffundvenueabout
Marie‐Josée Fleury, Guy Grenier, Jean-Marie Bamvita, Michel Perreault, Jean Caron

Bibliographic record

VenueGlobal Journal of Health Science · 2012
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteDouglas College
FundersCanadian Institutes of Health Research
KeywordsMental healthDiversity (politics)Catchment areaInclusion (mineral)Mental health serviceHealth professionalsService (business)PsychologySet (abstract data type)PsychiatryNursingMedicineHealth careGeographyPolitical scienceDrainage basinBusinessSocial psychology

Abstract

fetched live from OpenAlex

The study was designed to identify factors associated with the diversity of professionals consulted by 212 individuals affected by at least one mental disorder in the past 12 months in a Montreal catchment area. For inclusion in the study, participants had to be aged 15 to 65 and reside in the study zone. A comprehensive set of variables were analyzed in accordance with the Andersen's behavioural model of health service use. General practitioners, psychiatrists, and psychologists were the main professionals consulted in this study. Having post-secondary education, more than a single mental disorder, excellent relationships with neighbours, and (marginally) being a lifelong victim of violence were associated with higher numbers of professionals consulted. As this study highlights the large number of diversified professionals consulted for reason of mental disorders, shared care initiatives may prove beneficial. Greater effort could also be made in increasing services toward those deemed more vulnerable.

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 score0.034
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.085
GPT teacher head0.443
Teacher spread0.358 · 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

Citations9
Published2012
Admission routes4
Has abstractyes

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Same venueGlobal Journal of Health ScienceSame topicMental Health Treatment and AccessFrench-language works237,207