Achieving a climate for patient safety by focusing on relationships
Bibliographic record
Abstract
OBJECTIVE: Despite many initiatives, advances in patient safety remain uneven in part because poor relationships among health professionals have not been addressed. The purpose of this study was to determine whether relationships between health professionals contributed to a patient safety climate, after implementation of an intervention to improve inter-professional collaboration. DESIGN/SETTING: This was a secondary analysis of data collected to evaluate the Interprofessional Model of Patient Care (IPMPC) at The Ottawa Hospital in Ontario, Canada, which consists of five sites. A series of generalized estimating equation models were generated, accounting for the clustering of responses by site. PARTICIPANTS: Thirteen health professionals including physicians, nurses, physiotherapists and others (n = 1896) completed anonymous surveys about 1 year after the IPMPC was introduced. INTERVENTION: The IPMPC was implemented to improve interdisciplinary collaboration. MAIN OUTCOME MEASURES: Reliable instruments were used to measure collaboration, respect, inter-professional conflict and patient safety climate. RESULTS: Collaboration (β = 0.13; P = 0.002) and respect (β = 1.07; P = 0.03) were significant independent predictors of patient safety climate. Conflict was an independent and significant inverse predictor of patient safety climate (β = -0.29; P = 0.03), but did not moderate linkages between collaboration and patient safety climate or between respect and patient safety climate. CONCLUSIONS: Through the IPMPC, all health professionals learned how to collaborate and build a patient safety climate, even in the presence of inter-professional conflict. Efforts by others to foster better work relationships may yield similar improvements in patient safety climate.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| 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".