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Record W7128694986 · doi:10.7202/1123117ar

La santé mentale du personnel professionnel de l’éducation au Québec : revue narrative des risques psychosociaux du travail

2025· article· fr· W7128694986 on OpenAlexaffvenueabout
Carol-Anne Gauthier, Florence Côté, Élisabeth Proteau, Émilie Lessard-Mercier, François Bolduc

Bibliographic record

VenueNouveaux cahiers de la recherche en éducation · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicPsychodrama and Leishmaniasis Studies
Canadian institutionsUniversité de SherbrookeUniversité Laval
Fundersnot available
KeywordsNarrative reviewOccupational trainingContext (archaeology)Social environment

Abstract

fetched live from OpenAlex

Cet article a pour objectif de brosser un portrait de la santé mentale du personnel professionnel de l’éducation (PPE) au Québec: les risques psychosociaux du travail (RPS) et les éléments de contexte pouvant les expliquer. Un bref aperçu de l’historique des professions en éducation et du modèle de financement des services éducatifs complémentaires (SEC) permet de contextualiser les contradictions et les tensions dans le travail du PPE au Québec. Ensuite, nous présentons les résultats d’une revue de littérature narrative effectuée à partir d’un corpus de 38 articles scientifiques et 28 documents issus de la littérature grise afin de mieux comprendre les conditions d’exercice du travail du PPE. Ils démontrent comment la situation actuelle engendre des RPS – la surcharge de travail, le manque de reconnaissance et le manque de soutien des collègues et de la direction – qui peuvent affecter négativement la santé mentale du PPE. L’article conclut par une discussion critique des résultats, ainsi que des recommandations pour réduire les RPS, comme une refonte du financement des SEC, une meilleure intégration du PPE dans les équipes-écoles multidisciplinaires, et la pleine reconnaissance de leur expertise.

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)0.014
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.118
GPT teacher head0.434
Teacher spread0.316 · 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.

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

Citations0
Published2025
Admission routes3
Has abstractyes

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