‘Safety is about partnership’: Safety through the lens of patients and caregivers
Bibliographic record
Abstract
INTRODUCTION: Creating safer care is a high priority across healthcare systems. Despite this, most systems tend to focus on mitigating past harm, not creating proactive solutions. Managers and staff identify safety threats often with little input from patients and their caregivers during their health encounters. METHODS: This is a qualitative descriptive study utilizing focus groups and one-to-one interviews with patients and caregivers who were currently using (or had previously used) services in health systems across Canada. Data were analysed via inductive thematic analysis to understand existing and desired strategies to promote safer and better quality care from the perspectives of patients and caregivers. FINDINGS: In our analysis, we identified three key themes (safety strategies) from patients' and caregivers' perspectives and experiences: Using Tools and Approaches for Engaging Patients and Caregivers in their Care; Having Accountability Processes and Mechanisms for Safe Care; and Enabling Patients and Caregivers Access to Information. CONCLUSIONS: Safety is more than the absence of harm. Our findings outline a number of suggestions from patients and caregivers on how to make care safer, ranging from being valued on teams, participating as members of quality improvement tables, having access to health information, having access to an advocate to help make sense of information and having processes in place for disclosure and closure. Future work can further refine, implement and evaluate these strategies in practice. PATIENT OR PUBLIC CONTRIBUTIONS: An advisory group guided the research and was co-chaired by a patient partner. Members of the advisory group spanned patient and caregiver organizations and health sectors across Canada and included three patient partners and leaders who work closely with patients and caregivers in their day-to-day work. In the research itself, we engaged 28 patients and caregivers from across Canada to learn about their safety experiences and learn what safer care looks like from their perspectives.
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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 | 0.027 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.054 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".