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Record W2118772605 · doi:10.1258/jhsrp.2007.007123

Creating Receptor Capacity for Research in the Health System: The Executive Training for Research Application (Extra) Program in Canada

2008· editorial· en· W2118772605 on OpenAlexaffabout
Jean‐Louis Denis, Jonathan Lomas, Nina Stipich

Bibliographic record

VenueJournal of Health Services Research & Policy · 2008
Typeeditorial
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsCanadian Foundation for Healthcare ImprovementUniversité de Montréal
Fundersnot available
KeywordsTraining (meteorology)PsychologyMedical educationBusinessMedicineGeography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.028
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.972
Threshold uncertainty score0.720

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.056
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0040.003
Science and technology studies0.0100.008
Scholarly communication0.0150.004
Open science0.0090.002
Research integrity0.0520.053
Insufficient payload (model declined to judge)0.0080.003

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 head0.378
GPT teacher head0.588
Teacher spread0.210 · 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 designNot applicable
DomainIncentives
GenreEditorial

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

Citations32
Published2008
Admission routes2
Has abstractno

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