Predictors of perceived health care quality for registered nurses during and after health care reform
Bibliographic record
Abstract
BACKGROUND: Limited research has focused on the predictive nature of organizational culture and trust on registered nurses' perceived health care quality in reformed health care systems. PURPOSES: The purpose of this article was to investigate nurses' perceptions of organizational culture factors, trust in employer, and perceived health care quality during and 5 years after major organizational reform in the acute care setting and to test a model linking culture to perceived health care quality. METHODOLOGY: Survey data collected from two samples of nurses (N = 222,343) during and 5 years after major organizational reform in the acute care setting of one Canadian province were analyzed, and an exploratory model linking aspects of culture, trust, and quality was tested. FINDINGS: For both periods, most variable scores were in the low range and depicted moderately positive intercorrelations with each other. Support for the proposed model was mixed. Select culture variables predicted health care quality at both periods, but trust emerged as a significant predictor in 2000 only. The findings support the negative impact of system transformation on nurses and the link between culture and health care quality. PRACTICE IMPLICATIONS: The study findings suggest that managers and policy makers must develop and implement supportive and nurturing strategies that will enhance the organizational culture (emotional climate, collaborative relations), which should result in more positive perceptions of health care quality. However, further research is required to gain a better understanding of the relationships among trust, organizational culture, and perceptions of health care quality and what implications this may or may not have for nursing practice.
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 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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".