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Record W4410247571 · doi:10.3389/forgp.2025.1517251

Mitigating emotional exhaustion and disability claims: the roles of health and well-being climate and supervisor support

2025· article· en· W4410247571 on OpenAlexaffabout
Marie-Ève Beauchamp Legault, Denis Chênevert, Sari Mansour

Bibliographic record

VenueFrontiers in Organizational Psychology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversité de MontréalUniversité TÉLUQHEC MontréalUniversité Laval
Fundersnot available
KeywordsSupervisorEmotional exhaustionWell-beingPsychologyEmotional well-beingEmotional supportBurnoutApplied psychologyDevelopmental psychologySocial psychologySocial supportClinical psychologyPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

Introduction This study examines the effect of the Health and Well-being Climate (HWC) and the moderating role of supervisor support on employees' emotional exhaustion and insurance claims, grounded in the Conservation of Resources theory. Methods Quantitative data were collected from 661 employees across 17 organizations in Canada, and group insurance claims data for these employees were also analyzed. Structural equation modeling was conducted using IBM SPSS AMOS 28.0 to test all hypotheses. Findings The results indicate that a positive perception of the HWC among employees reduces both emotional exhaustion and the number of disability claims. Furthermore, supervisor support moderates the relationship between HWC and emotional exhaustion. High levels of emotional exhaustion and disability claims from employees can result in significant direct and indirect costs for employers. This study aims to provide organizations, managers, and practitioners with insights into effective strategies for mitigating these costs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.045
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.373
Teacher spread0.354 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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