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Record W2099512001 · doi:10.1080/13561820802490776

Interdisciplinary primary health care research training through TUTOR-PHC: The insiders' view

2009· article· en· W2099512001 on OpenAlexaffabout
Kadija Perreault, Antoine Boivin, Enette Pauzé, Amanda Terry, Christie Newton, Sue Dawkins, Janie Houle, Judith Belle Brown

Bibliographic record

VenueJournal of Interprofessional Care · 2009
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in RehabilitationWestern UniversityUniversity of TorontoUniversity of British ColumbiaCégep de l'Abitibi Témiscamingue
Fundersnot available
KeywordsTUTORTraining (meteorology)Medical educationPrimary health carePrimary careNursingPsychologyHealth careMedicinePedagogyFamily medicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

Recent policy initiatives in Canada have highlighted the lack of research capacity among most disciplines involved in primary health care, resulting in a majority of clinical and health services re...

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)0.033
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.014
Scholarly communication0.0160.007
Open science0.0030.005
Research integrity0.0140.013
Insufficient payload (model declined to judge)0.0190.002

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 head0.253
GPT teacher head0.572
Teacher spread0.319 · 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.

Study designQualitative
DomainMethods
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
Published2009
Admission routes2
Has abstractyes

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