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Record W2051569330 · doi:10.1177/0163278702250097

Development Of The Approaches To Work And Workplace Climate Questionnaires For Physicians

2003· article· en· W2051569330 on OpenAlex
John R. Kirby, M. Dianne Delva, Christopher K. Knapper, Richard Birtwhistle

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEvaluation & the Health Professions · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsQueen's University
Fundersnot available
KeywordsWork environmentWork (physics)Organisation climateMedical educationPsychologyMedicineJob satisfactionEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Two questionnaires were developed to investigate the workplace learning of physicians. The Approaches to Work Questionnaire for Physicians and the Workplace Climate Questionnaire for Physicians were adapted from general measures developed by Kirby, Knapper, Evans, Carty, and Gadula. These questionnaires were administered to a random sample of Ontario physicians. Consistent with the results of Kirby et al., three dimensions of approaches to work were observed: Deep. Surface-Rational, and Surface-Disorganized. Three dimensions of workplace climate were also found, Supportive-Receptive, Choice-Independence, and Workload. Results indicate that physicians adopt primarily a Deep approach to work, but that there is a smaller tendency toward Surface-Disorganized learning, one that is strongly correlated with perceptions of heavy workload. The Deep approach was associated with work environments perceived to be Supportive-Receptive and offer Choice-Independence. The use of these questionnaires in research and practice is discussed.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.346
GPT teacher head0.483
Teacher spread0.137 · 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