The role of career counselling in supporting career well-being of nurses
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".