MétaCan
Menu
Back to cohort
Record W2762448999 · doi:10.23937/2469-5793/1510055

The Intervisions Cliniques Continuing Medical Education Program: A Forum for Exchange and Mutual Knowledge Development between General Practitioners and Psychiatrists

2017· article· en· W2762448999 on OpenAlexaffabout
Catherine Briand

Bibliographic record

VenueJournal of Family Medicine and Disease Prevention · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de MontréalInstitut Universitaire en Santé Mentale de QuébecHôpital Louis-H Lafontaine
FundersEli Lilly and Company
KeywordsSet (abstract data type)Context (archaeology)Mental healthMedical educationContinuing medical educationContinuing educationNursingMedicinePsychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

The findings help to define the context and vision in which the Intervisions cliniques program was set up and implemented, to identify the perceived benefits and disappointments with regard to the program. Set up in the Canadian public managed care system and university-affiliated hospital, the Intervisions cliniques program helps to create lines of communication and privileged exchanges between general practitioners and psychiatrists as well as to foster mutual knowledge and a crosscutting and interactive view within a shared clinical territory. Several benefits noted by the participants and the organizing committee demonstrate the importance and multimodal effects of this type of initiative. The case-discussion format used as a CME method meets the training needs of general practitioners and is widely appreciated. However, the pressing needs expressed by the general practitioners with regard to improving shared mental health care still pose many challenges.

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)0.019
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.003
Scholarly communication0.0030.002
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.302
GPT teacher head0.566
Teacher spread0.265 · 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 designNot applicable
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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Family Medicine and Disease PreventionSame topicMental Health and Patient InvolvementFrench-language works237,207