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Record W1171318658 · doi:10.3233/wor-2012-0128-5

Innovation in the occupational health physician profession requires the development of a work collective to improve the efficiency of MSD prevention

2012· article· en· W1171318658 on OpenAlexaff
Sandrine Caroly, Aurélie Landry, Céline Cholez, Philippe Davezies, Marie Bellemare, Nadine Poussin

Bibliographic record

VenueWork · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDisciplinePublic relationsOccupational safety and healthWork (physics)PopulationMedicinePsychologyMedical educationNursingSociologyEngineering ethicsPolitical scienceEnvironmental healthEngineeringSocial sciencePathology

Abstract

fetched live from OpenAlex

Given the ageing population of occupational health physicians and the deteriorating situation of employee health, reforms targeting the multi-disciplinary nature of occupational health are currently being drawn up. These are of great concern to doctors in terms of the future of occupational health, notably with regard to changing medical practices. The objective of this study is to explore the actual practices of occupational health physicians within the framework of MSD prevention in France. By analysing the activity of occupational health physicians, we could gain a better understanding of the coordination between those involved in OHS with the ultimate goal being to improve prevention. Based on an analysis of peer activity, this method made it possible to push beyond pre-constructed discourse. According to activity theories, it is through others that the history and controversies of a profession can be grasped and skills developed. The results produced by these collective discussions on activity analysis contributed to establish a collective point of view about the important aspects of their profession that need defending and the variations in professional genre in relation to the current reforms, notably.

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 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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.088
GPT teacher head0.458
Teacher spread0.371 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2012
Admission routes1
Has abstractyes

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