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Record W2112094190 · doi:10.12927/hcpol.2007.18528

Knowledge Translation through Research-Based Theatre

2006· article· en· W2112094190 on OpenAlexaffvenueabout
Joan M. Eakin, Marion Endicott

Bibliographic record

VenueHealthcare policy · 2006
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsToronto Public HealthPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsKnowledge translationWork (physics)Focus (optics)Knowledge managementPublic relationsMedical educationSociologyComputer sciencePolitical scienceMedicineEngineering

Abstract

fetched live from OpenAlex

This case documents the metamorphosis of an academic public health project on the implementation of Ontario's return-to-work policies and practices into a community theatre project, the production of a play called Easy Money. The result was a highly successful knowledge translation initiative. Injured workers, the focus of the academic study, intensely identified with the play and were given hope that their individual experiences could be broadly translated. For the researchers, the undertaking provided new perspectives on the original research problem, validated their original findings and generated numerous topics for subsequent research.

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)0.059
metaresearch head score (Gemma)0.084
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 score0.059
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.084
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0090.024
Scholarly communication0.0150.014
Open science0.0040.018
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0180.005

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 head0.188
GPT teacher head0.469
Teacher spread0.281 · 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

Citations24
Published2006
Admission routes3
Has abstractyes

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