MétaCan
Menu
Back to cohort
Record W2805571587 · doi:10.1002/cjas.1500

A Measure and Model of Psychological Health and Safety in the Workplace that Reflects Canada's National Standard

2018· article· en· W2805571587 on OpenAlexvenueaboutno aff
Gary W. Ivey, J.‐R. Sébastien Blanc, Kathy Michaud, Tzvetanka Dobreva‐Martinova

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2018
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsMajestyConceptual modelPsychologyApplied psychologyQueen (butterfly)Mental healthPolitical sciencePublic relationsComputer scienceLawPsychiatry

Abstract

fetched live from OpenAlex

Abstract To monitor workplace factors associated with well‐being and performance in the Department of National Defence, and embodying departmental efforts to create and maintain a healthy workplace, we assembled a comprehensive and psychometrically sound survey battery that reflects Canada's national standard for psychological health and safety. Moreover, for testing and understanding the relationships among the survey variables and national standard factors, we applied an evidence‐based framework to build a conceptual psychological health and safety model. In this article, we introduce our survey and model to other organizations and the wider academic community, we provide some preliminary support for the pattern of results illustrated in our climate profiles, and we discuss our future research agenda to address current limitations. © 2018 Her Majesty the Queen in Right of Canada. Canadian Journal of Administrative Sciences © 2018 ASAC. Published by John Wiley & Sons, Ltd. Reproduced with the permission of the Minister of National Defence.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.199
GPT teacher head0.441
Teacher spread0.242 · 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 designTheoretical or conceptual
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

Citations15
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l AdministrationSame topicWorkplace Health and Well-beingFrench-language works237,207