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Record W4399376851 · doi:10.7202/1111854ar

Effet des pratiques ressources humaines efficaces sur la satisfaction au travail : le rôle de l’engagement au travail et de l’identification organisationnelle

2024· article· fr· W4399376851 on OpenAlexaff
Martin Lauzier, Guillaume Desjardins

Bibliographic record

VenueHumain et Organisation · 2024
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsPolitical sciencePhilosophySociologyHumanities

Abstract

fetched live from OpenAlex

S’appuyant sur le modèle AMO (Ability, Motivation, Opportunity) et la Théorie de l’identité sociale, cette étude vise à mieux saisir les mécanismes et conditions d’influence de la relation entre les pratiques ressources humaines (RH) efficaces et la satisfaction au travail. Basée sur les réponses offertes par 201 employés qui ont rempli un sondage électronique durant la pandémie de COVID-19, cette étude apporte trois contributions. Premièrement, elle relève l’effet positif des pratiques RH efficaces sur les niveaux d’engagement et de satisfaction des employés. Deuxièmement, elle souligne le rôle médiateur de l’engagement dans la relation unissant les pratiques RH efficaces à la satisfaction au travail. Troisièmement, elle montre le rôle modérateur de l’identification organisationnelle sur cette première relation. Les implications de ces résultats sont discutées en guise de conclusion.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.018
GPT teacher head0.271
Teacher spread0.252 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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