Exploring service climate in healthcare using a change management approach
Bibliographic record
Abstract
Understanding nurses’ perception of service climate in a resource-constrained environment undergoing continuous change is important as patients seek patient-centered health services. Unfortunately, organizational change approaches influencing service climate have yet to be explored. The study’s objectives included: (1) assessing nurses’ perceptions of service climate (orientation, feedback, and managerial practices) and their strategic change mindsets (Theories E, O, and EO) in a post-restructuring environment; and (2) determining the best model of fit, based on organizational dimensions of change and strategic change mindsets, for service climate. A cross-sectional survey collected responses from nurse members of a professional association. Linear regression analysis was used to obtain the service climate models. The findings revealed nurses’ perception of service climate was positive, except for managerial practices. The predominant mindset was Theory EO (balance between Theory E – economic value and Theory O – organizational capabilities). Capacity to change and learn, managerial practices and behaviour, and position level positively predicted service climate; the best model of fit was for nurses who adopted a Theory EO mindset. In conclusion, to enhance nurses’ perception of service climate, leaders/managers need to provide recognition and rewards for high-quality service and use a Theory EO approach (balance financial performance with internal capabilities).
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".