MétaCan
Menu
Back to cohort
Record W2066962607 · doi:10.1002/chp.1340230507

Short-term educational intervention improves family physicians' knowledge of depression

2003· article· en· W2066962607 on OpenAlexaff
Stan Kutcher, Bianca Lauria-Horner, Connie MacLaren, Maja Bujas‐Bobanovic, Zlatko Karlovic

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2003
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMontreal General HospitalMedtronic (Canada)Nova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsDepression (economics)Intervention (counseling)Test (biology)MedicineFamily medicineEducational programPrimary carePsychiatryPsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Depression is frequently unrecognized and undertreated. Therefore, there is a need to increase the knowledge and skills of primary care physicians regarding management of depression. The aim of this study was to determine if a brief educational intervention can affect family physicians' knowledge of the diagnosis and treatment of depression. METHOD: Sixty-eight community-based, nonacademic family physicians completed the program, which was delivered using a mixed lecture-seminar format. Knowledge about depression was assessed pre- and post-program. Paired-sample t test and chi-square test were used to compare test scores. RESULTS: Although study physicians demonstrated high baseline knowledge of depression, 75% of them had better scores following the program. The increase in knowledge was statistically significant (p < .0001). DISCUSSION: Our study demonstrates that a simple and brief educational program can enhance family physicians' knowledge of depression; however, an increase in knowledge alone may not necessarily translate into practice behavior change.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.041
GPT teacher head0.468
Teacher spread0.426 · 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

Citations7
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Continuing Education in the Health ProfessionsSame topicMental Health Treatment and AccessFrench-language works237,207