The object of your affection: how commitment, leadership and justice influence workplace behaviours in health care
Bibliographic record
Abstract
AIM: This paper describes the development of a coherent framework that develops nursing knowledge and guides research in workplace behaviours, work performance, and the factors that influence behaviours and performance. BACKGROUND: Work performance is dependent upon behaviours that are related to one's commitment towards their workplace and leadership interactions. The influence of these concepts on work outcomes has been established in disparate studies, but their precedence in terms of influencing workers' behaviours, is not well understood. METHODS: A scientific realism approach is applied, where theory and current research in the field of organisational behaviour and work motivation are drawn upon to identify validated constructs and explain their relationships. DISCUSSION: An augmented framework is produced, incorporating concepts of relevance to work motivation and work attitudes. Propositions, predicated on research evidence, are offered. Conclusions A novel comprehensive framework is developed, extending the range of behaviours important to workers and the organisation. IMPLICATIONS FOR NURSING MANAGEMENT: Focusing on targets for which nurses are affectively committed can prove useful to managers. The developed framework can be informative to managers by increasing awareness of the relationships between concepts, such that they are mindful of these constructs while interacting with staff.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".