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THE RELATIONSHIP BETWEEN OCCUPATIONAL STRESSORS AND COPING STRATEGIES IN NURSING TECHNICIANS

2016· article· en· W2567500671 on OpenAlexaff
Sandra de Souza Pereira, Carla Araújo Bastos Teixeira, Emilene Reisdorfer, Mariana Verderoce Vieira, Edilaine Cristina da Silva Gherardi‐Donato, Lucilene Cardoso

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Burnout
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStressorCronbach's alphaCoping (psychology)Descriptive statisticsPsychologyOccupational stressNursingDescriptive researchExploratory researchNursing researchClinical psychologyMedicinePsychometrics

Abstract

fetched live from OpenAlex

ABSTRACT This is a quantitative, descriptive and exploratory research, with cross-sectional design that investigated the stressors experienced by nursing technicians working in general hospital and identified the coping strategies most used by them. The sample contained 310 participants. A sociodemographic questionnaire and the Ways of Coping Scale were used. For the analysis we used descriptive statistics and calculated the Cronbach's alpha. 60% of professionals used the strategies focused on the problem; 57.4% attributed their stress to working conditions, 26.8% to relationships in the workplace, 5.5% to the lack of reward at work and only 0.6% to problems personal. We conclude that strategies focused on the problems were the most used, indicating an approximation of the stressor in order to fix it. The identified stressors indicate the need for planning, stimulating and recognizing nursing professionals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.585
GPT teacher head0.599
Teacher spread0.014 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2016
Admission routes1
Has abstractyes

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