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Record W2086890727 · doi:10.1016/j.burn.2015.04.002

Revisiting the interplay between burnout and work engagement: An Exploratory Structural Equation Modeling (ESEM) approach

2015· article· en· W2086890727 on OpenAlex
Sarah‐Geneviève Trépanier, Claude Fernet, Stéphanie Austin, Julie Ménard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBurnout Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité du Québec à Montréal
Fundersnot available
KeywordsWork engagementBurnoutPsychologyStructural equation modelingAutonomySocial psychologyOccupational stressEmployee engagementScale (ratio)Work (physics)Clinical psychologyManagement

Abstract

fetched live from OpenAlex

This study aimed to investigate the interplay between burnout and work engagement. More specifically, we examined the energy and identification continua theorized to underlie the relationship between burnout and work engagement by simultaneously evaluating the factorial structure of the Maslach Burnout Inventory–General Survey (MBI–GS) and the Utrecht Work Engagement Scale (UWES). Results from Exploratory Structural Equation Modeling (ESEM) offered little support for these continua, suggesting that burnout and work engagement are not diametrical counterparts. Moreover, ESEM significantly altered the relationships burnout and work engagement hold with job demands and resources (i.e., work overload, job autonomy, and recognition), as well as health-related (i.e., psychological distress) and motivational (i.e., turnover intention) outcomes. These findings shed new light on the health-impairment and motivational processes theorized by the JD-R model.

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.

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)0.022
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.004
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 head0.432
GPT teacher head0.537
Teacher spread0.105 · 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