Examination of the relationship between management and clinician agreement on communication openness, teamwork, and patient satisfaction in the US hospitals
Bibliographic record
Abstract
Background: Patient satisfaction has always been an area of focus for hospitals, but gained particular importance due to the changes in the Centers for Medicare and Medicaid reimbursement policies. Hospital managers and clinicians interact with patients in different ways and have different perspectives on safety culture, yet little is known about how that impacts patient satisfaction.Objective: To examine how the agreement between clinicians and management perspectives on patient safety culture is related to patient satisfaction by employing cross-sectional design with linear regressions.Methods: Two data sets were used: 2012 Hospital Survey on Patient Safety Culture and 2012 Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). The dependent variables were: overall rating of the hospital experience and willingness to recommend a hospital. The independent variables were four safety culture domains: communication openness, feedback, and communication about errors, teamwork within units, and teamwork between units.Results: The results suggest that manager and clinician agreement on high levels of communication openness, feedback and communication about errors, teamwork between units, and teamwork across units were positively and significantly associated with overall patient satisfaction and willingness to recommend. Additionally, more favorable perceptions about patient safety culture by only clinicians yielded similar findings.Conclusions: For policymakers, measuring managers and clinicians’ perceptions on patient safety culture may provide a valuable indicator of patient satisfaction throughout the country. While managers are more likely to have the power to devote resources to patient safety initiatives, they may not adequately judge culture in their unit and should take into account the perspectives of clinicians who have a more frontline perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".