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Record W7100872798

BURNOUT AMONG NURSING STAFF AND INTENTION TO LEAVE THE PROFESSION: VALIDATION OF THE JOB DEMANDS-RESOURCES MODEL IN HOSPITALS IN QUÉBEC 7,8

2013· article· en· W7100872798 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutDepersonalizationEmotional exhaustionContext (archaeology)Occupational burnoutNursing staffJob satisfactionSocial support
DOInot available

Abstract

fetched live from OpenAlex

This study examines the role of burnout in the relationship between stress factors related to work and the social environment and intention to leave the profession. Based on a sample of 1,636 nurses, the results suggest that demands indirectly induce depersonalization via emotional exhaustion, whereas resources are associated with a more humane treatment of patients. In addition, psychosomatic complaints and affective professional commitment partly mediate the impact of burnout on intention to leave the profession. If the first case of burnout among nurses reported in the literature dates back over fifty years (Schwartz & Will, 1953), burnout is still afflicting these workers (Aiken et al., 2001; International Council of Nurses, 2006; Kerr et al., 2002), although research programs launched in the mid-70s have shed considerable light on this syndrome (Maslach, Schaufeli & Leiter, 2001). If the relationship to the patient, characterized as emotionally demanding, has long been recognized as a source of chronic stress that plays a determining role in the development of burnout among nurses (Maslach, 1979; Maslach & Jackson, 1982; Williams, 1989), more recent research has documented the impact of a broader range of factors linked to the organization of work, the social environment and the organizational context (for a

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)0.000
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 score0.037
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.314
Teacher spread0.297 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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