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Record W1598700275 · doi:10.4000/pistes.4038

L’identité professionnelle des gestionnaires des organisations de la santé : un sale boulot requérant un courage managérial ?

2014· article· fr· W1598700275 on OpenAlexvenueno aff
Sylvie St‐Onge, Line Provost

Bibliographic record

VenuePerspectives interdisciplinaires sur le travail et la santé · 2014
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Bien que les gestionnaires des organisations de soins doivent relever des défis croissants, cette profession semble souvent dépréciée et critiquée. En s’appuyant sur les concepts d’« identité sociale » et d’« identité professionnelle » de même que sur celui de « sale boulot », notre étude explore les menaces identitaires vécues par les gestionnaires de la santé et les stratégies qu’ils déploient afin de les surmonter. Les résultats confirment que les gestionnaires de la santé participants, surtout ceux qui ne sont pas médecins, perçoivent être considérés comme des bureaucrates sans cœur et inefficients, tant au sein de leur établissement qu’à l’extérieur de celui-ci. Ils tendent à combattre ces stigmates en recourant à diverses stratégies offensives et défensives. En outre, d’une part, ils cherchent à expliquer leur valeur ajoutée et leur « humanité » et d’autre part, ils valorisent le courage managérial que nécessite la prise de décisions difficiles dans leur emploi.

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)0.015
metaresearch head score (Gemma)0.020
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: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0130.025
Scholarly communication0.0160.012
Open science0.0020.008
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0130.002

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 head0.009
GPT teacher head0.276
Teacher spread0.267 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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