MétaCan
Menu
Back to cohort
Record W2046268784 · doi:10.1007/s10488-011-0368-6

Developing Effective Research-Practice Partnerships for Creating a Culture of Evidence-Based Decision Making

2011· article· en· W2046268784 on OpenAlexaff
Manuel Riemer, Susan Douglas Kelley, Susan E. Casey, Katherine Taylor Haynes

Bibliographic record

VenueAdministration and Policy in Mental Health and Mental Health Services Research · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWilfrid Laurier University
FundersNational Institute of Mental HealthNational Institutes of HealthVanderbilt University
KeywordsGeneral partnershipService providerObstacleFoundation (evidence)Service (business)Public relationsMental healthKnowledge managementKey (lock)Process managementEvidence-based practiceBusinessComputer sciencePsychologyMedical educationMedicinePolitical scienceMarketingAlternative medicineComputer security

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)0.576
metaresearch head score (Gemma)0.585
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5760.585
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0090.007
Science and technology studies0.0260.054
Scholarly communication0.0690.058
Open science0.0140.078
Research integrity0.0300.049
Insufficient payload (model declined to judge)0.0100.003

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 head0.844
GPT teacher head0.759
Teacher spread0.085 · 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
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

Citations24
Published2011
Admission routes1
Has abstractno

Explore more

Same venueAdministration and Policy in Mental Health and Mental Health Services ResearchSame topicHealth Policy Implementation ScienceFrench-language works237,207