Enhancing nurses well-being through managerial coaching: a mediating model
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Purpose Workplace well-being has emerged as an important aspect in the field of health care. Therefore, this paper aims to investigate the role of managerial coaching on nurses’ well-being through psychological ownership and organizational identity. Design/methodology/approach The authors approached 284 nurses working in both public and private hospitals (between December 2019 and February 2020) on convenience basis, and data were collected through an online questionnaire-based survey. Findings The data were analyzed using AMOS version 24 and structural equation modeling confirmed psychological ownership and organizational identity as explanatory variables between managerial coaching and well-being. Research limitations/implications The study used self-reported data using convenience sampling which may raise a question on causality. The findings suggest the management to consider the importance of managerial coaching in shaping positive workplace behaviors of employees. Originality/value Drawings on social exchange theory, this study extends past studies to examine the mediating roles of psychological ownership and organizational identification between managerial coaching and workplace well-being among nurses. The study has theoretical and practical implications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".