MétaCan
Menu
Back to cohort
Record W4385393182 · doi:10.1007/s43477-023-00094-3

Building a High-Quality Mental Health and Addictions System: How Do Intermediary Organizations Contribute?

2023· article· en· W4385393182 on OpenAlexaff
Janet Durbin, Avra Selick, Maryann Roebuck, Rossana Coriandoli, Gordon Langill, Lisa B. Dixon, Tamara Sale, Catherine Briand, Richard Kruszynski

Bibliographic record

VenueGlobal Implementation Research and Applications · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaUniversité du Québec à Trois-RivièresUniversity of TorontoUniversité du Québec à MontréalCentre for Addiction and Mental Health
Fundersnot available
KeywordsInitial public offeringMental healthBusinessIncentiveEvidence-based practicePsychosocialGovernment (linguistics)PsychologyPublic relationsMedicineAccountingPolitical science

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)0.169
metaresearch head score (Gemma)0.200
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.169
Threshold uncertainty score0.892

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1690.200
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0120.015
Scholarly communication0.0380.032
Open science0.0090.029
Research integrity0.0120.015
Insufficient payload (model declined to judge)0.0260.003

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 head0.399
GPT teacher head0.699
Teacher spread0.300 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractno

Explore more

Same venueGlobal Implementation Research and ApplicationsSame topicHealth Policy Implementation ScienceFrench-language works237,207