Patient engagement study to identify and improve surgical experience
Bibliographic record
Abstract
BACKGROUND: Patient engagement is the establishment of active partnerships between patients, families, and health professionals to improve healthcare delivery. The objective of this project was to conduct a series of patient engagement workshops to identify areas to improve the surgical experience and develop strategies to address areas identified as high priority. METHODS: Faculty surgeons and patients were invited to participate in three in-person meetings. Evaluation included identifying and developing strategies for three priority areas to improve the surgical experience and level of engagement achieved at each meeting. RESULTS: Sixteen faculty surgeons and 32 patients participated. Some 63 themes to improve the surgical experience were identified; the three highest-priority themes were physician communication, discharge process, and expectations at home after discharge. Individual improvement strategies for these three prioritized themes (12, 36 and 6 respectively) were used to develop a formal strategic plan, and included a physician communication survey, discharge process worksheet and video, and guideline regarding what to expect at home after discharge. Overall, the level of engagement achieved was considered high by over 85 per cent of the participants. CONCLUSION: A high level of patient engagement was achieved. Priorities were identified with patients and surgeons to improve surgical experience, and strategies were developed to address these areas.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".