ISQUA16-2464HOW PATIENTS-AS-PARTNERS CAN HELP INCREASE PATIENT SAFETY AT THE BEDSIDE
Bibliographic record
Abstract
To advocate for patients, particularly those with chronic illness, to be more actively involved with the healthcare services they receive, the Faculty of Medicine of the University of Montreal (UM) in Canada and its affiliated hospitals developed the Patients as Partners concept, where the patient is considered a full-fledged partner of the healthcare delivery team and the patient's experiential knowledge is recognized. This study aims to illustrate how patients interact with their healthcare professionals to reduce patient safety incidents. Using theoretical sampling, we conducted 18 semi-structured interviews with patients who train health sciences students at UM on the concept of patients as partners. For this study, participants had to have participated in at least one interprofessional collaboration course at UM in the previous year and completed a training course on the concepts of partnership of care. Since participants were selected based on their familiarity with the concepts, they were able to talk about them with respect to their own experience of care. The interviews were semi-structured and covered: 1) whether they had been through an incident or accident, or had avoided either one, in connection with their treatment or that of a close one; 2) how they had applied the patient-as-partner concept in such situations; 3) how the team had reacted; 4) how partnership of care can help minimize incidents and accidents in the healthcare system.
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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 | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.170 | 0.027 |
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".