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Record W2124996542 · doi:10.7870/cjcmh-2008-0027

Behavioural Interventions in Primary Care: An Implementation Trial

2008· article· en· W2124996542 on OpenAlexaffvenue
Dan Bilsker, John F. Anderson, Joti Samra, Elliot M. Goldner, David L. Streiner

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of TorontoUniversity of VictoriaSimon Fraser University
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)MedicineMental healthDepression (economics)Brief interventionPrimary careRandomized controlled trialSession (web analytics)Family medicineNursingPsychiatry

Abstract

fetched live from OpenAlex

Developing effective strategies to keep health care providers' practice current with best practice guidelines has proven to be challenging. This trial was conducted to determine the potential for using brief educational sessions to generate significant change in physician delivery of mental health and substance use interventions in primary care. A 1-hour educational session outlining interventions for depression and risky alcohol use was delivered to a sample of 85 family physicians. The interventions used a supported self-management approach and included free patient access to appropriate selfmanagement resources. The study initially evaluated physicians' implementation of these interventions over a 2-month period. Physician uptake of the depression intervention was significantly greater than uptake of the risky-drinking intervention (32% versus 10%). A follow-up at 6-months posttraining (depression intervention only) demonstrated fairly good maintenance of intervention delivery. Implications of these findings are discussed.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0110.001

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 head0.279
GPT teacher head0.512
Teacher spread0.233 · 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 designNon-randomized trial
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

Citations10
Published2008
Admission routes2
Has abstractyes

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