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Record W2127488449 · doi:10.7202/032425ar

Validation d’un modèle de déterminants psychosociaux de la santé au travail de l’infirmière en gériatrie

2007· article· fr· W2127488449 on OpenAlexaffvenueabout
André Duquette, S Kérouac, Balbir K. Sand, Pierre Saulnier, Lise Lachance

Bibliographic record

VenueSanté mentale au Québec · 2007
Typearticle
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsCanadian Nurses AssociationUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPsychologyPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

The purpose of this study was to verify a model of relationships between psychosocial factors and health for 8066 francophone nurses working in geriatric care in Québec. A random sample of 1990 subjects was drawn and a participation rate of 77.9% and 55% was obtained for the two-time study taken twelve months apart. Based on the theory of Maddi and Kobasa (1984), the model was reproduced for the two-time periods with the aid of structural equations. The analyses showed that three variables exert a direct influence on psychological distress: professional burnout, occupational stressors and hardiness. Also, variables have a direct effect on burnout: listed in order of importance, these are hardiness, occupational stressors, work support, active strategies of coping and employment status. In dealing with the work stressors, the nurses who are hardy make use of active strategies of coping and look for support form their colleagues. The results of the study help to better understand the psychological and social resources that best favor adaptation of working women in highly demanding work environments. The fallout of the study converges towards the quality of life of helping professionals and towards the cost and quality of health and social services.

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)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.403
Teacher spread0.374 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2007
Admission routes3
Has abstractyes

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