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Record W2971221294 · doi:10.1111/jonm.12857

The long‐term effects of psychological demands on chronic fatigue

2019· article· en· W2971221294 on OpenAlexafffundabout
Parise LeGal, Ann Rhéaume, Jane Mullen

Bibliographic record

VenueJournal of Nursing Management · 2019
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsMount Allison UniversityUniversité de MonctonUniversity of New Brunswick
FundersUniversité de Moncton
KeywordsPsychological interventionChronic fatigueNursing managementNursingSocial supportPsychologyMedicineApplied psychologyChronic fatigue syndromeSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

AIM: Investigate the impact of psychological job demands and resources on chronic fatigue. BACKGROUND: Nurse fatigue is a serious problem with negative consequences on patient safety and nurse well-being. Excessive job demands can exacerbate nurse fatigue, which may limit the ability of nurses to engage in professional practice. METHODS: This two-wave study was carried out with a self-report questionnaire administered to nurses in eastern Canada (n = 154). Cross-lagged analysis using structural equation modelling was conducted to examine the interactions between psychological job demands, resources and chronic fatigue over time. RESULTS: Results showed that psychological job demands predicted chronic fatigue a year later. Nonetheless, job resources (decision latitude, social support) did not buffer the relationship between psychological job demands and chronic fatigue 1 year later. CONCLUSIONS: Psychological demands have a long-term effect on chronic fatigue, thus interventions to mitigate fatigue are needed. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers should be aware of the cumulative effects of chronic fatigue and implement strategies to create a better balance between job demands and resources in the workplace.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.373

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 head0.030
GPT teacher head0.368
Teacher spread0.339 · 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

Citations19
Published2019
Admission routes3
Has abstractyes

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