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Implementation process evaluation of an ergonomic train the trainer program: How to learn from mechanisms and the temporal structure of processes?

2023· article· en· W4315880901 on OpenAlexaff
Émilie Desjardins, Hélène Sultan‐Taïeb, France St‐Hilaire, Nicole Vézina, Élise Ledoux, Rita Naji, Patricia Bélanger

Bibliographic record

VenueEvaluation and Program Planning · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de SherbrookeUniversité du Québec à Montréal
Fundersnot available
KeywordsTimelineTrainerProcess (computing)Context (archaeology)Process managementIntervention (counseling)Psychological interventionHuman factors and ergonomicsKnowledge managementComputer scienceEngineeringApplied psychologyRisk analysis (engineering)Poison controlPsychologyMedicine

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)0.233
metaresearch head score (Gemma)0.417
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.233
Threshold uncertainty score0.946

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2330.417
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.004
Science and technology studies0.0030.003
Scholarly communication0.0090.019
Open science0.0030.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.480
GPT teacher head0.670
Teacher spread0.190 · 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
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

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