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Record W3036699288 · doi:10.7202/1069717ar

La méthodologie du PCAO : une approche de recherche-action au service de la réflexion collective des équipes de direction en contexte de changement organisationnel

2020· article· fr· W3036699288 on OpenAlexaffvenueabout
Julie Béliveau, Anne-Marie Corriveau

Bibliographic record

VenueEnjeux et société Approches transdisciplinaires · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesPolitical scienceCollective actionSociologyPhilosophyPolitics

Abstract

fetched live from OpenAlex

Bien que la réflexion collective soit souhaitée et souhaitable, elle s’avère souvent difficile à réaliser pour les équipes de direction des établissements du réseau de la santé qui sont aux prises avec des changements importants et constants. Cet article illustre que la recherche-action, et plus précisément la méthodologie du parcours collectif d’apprentissage organisationnel (PCAO), peut contribuer à soutenir cette réflexion collective. L’analyse des données tirées d’un projet de recherche mené auprès de cinq établissements de santé et de services sociaux du Québec permet d’établir de quelles façons. Plus concrètement, cet article explique comment se construit la réflexion collective des équipes de direction à travers les différentes étapes du PCAO. En démontrant que cette réflexion mène à un véritable apprentissage organisationnel, les résultats de l’étude positionnent le PCAO comme une méthode de recherche-action non seulement utile du point de vue de la recherche, mais aussi comme approche de développement organisationnel.

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)0.059
metaresearch head score (Gemma)0.091
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: none
Teacher disagreement score0.059
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.091
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0070.008
Science and technology studies0.0060.020
Scholarly communication0.0190.015
Open science0.0050.011
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0160.004

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 head0.355
GPT teacher head0.462
Teacher spread0.107 · 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".

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Citations0
Published2020
Admission routes3
Has abstractyes

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