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Record W1979192000 · doi:10.1177/1038416214555772

The role of career counselling in supporting career well-being of nurses

2015· article· en· W1979192000 on OpenAlexaff
Charles P. Chen, Sarah Haller

Bibliographic record

VenueAustralian Journal of Career Development · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBurnoutNursingCareer developmentPerspective (graphical)Coping (psychology)PsychologyPsychological interventionMedical educationMedicineClinical psychology

Abstract

fetched live from OpenAlex

The phenomenon of occupational and career burnout in nurses has received recent attention from academia, the media, and health care practitioners. Research surrounding career burnout often adopts a health perspective and focuses on the psychological well-being of nurses. While acknowledging the vital importance of a health perspective, this article contends that the ability to cultivate a sense of career well-being may act as an antidote to the occupational and career burnout in the nursing profession. To examine the relationship between career burnout and career well-being in nurses, the article explores the many ways career counsellors can be of service to clients in the nursing profession, improving clients’ career well-being via the enhancement of effective coping skills. In particular, the phenomenon of career burnout and its related issues and factors in nurses are identified and analysed. Guided by key tenets from career development theoretical approaches, counselling interventions are proposed to address the unique occupational burnout issue in the nursing profession, aiming to further the career well-being of nurses.

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)0.005
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 score0.683
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.401
Teacher spread0.310 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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