‘Spending the day with your Family Health Team’: rapid ethnography of a patient-centred quality improvement event
Bibliographic record
Abstract
BACKGROUND: Primary care practices have started to explore different methods of engaging with patients to advance quality improvement. This approach leverages the strengths of citizen engagement; however, there has been a lack of empirical research to understand the impact of such an approach from the patient perspective. AIM: To understand how citizen engagement can inform quality improvement in family practice. DESIGN & SETTING: A single-centre, rapid ethnographic evaluation of a patient engagement event. METHOD: Ten thousand email invitations were sent and posters put up in Family Health Team (FHT) waiting rooms, resulting in 350 patient responses and the purposive recruitment of 36 participants. Observation and key informant interviews were used to collect data. The data corpus was analysed according to ethnographically-informed thematic analysis techniques. RESULTS: Analysis of the full set of field notes, patient interviews, and informal conversations with the FHT staff revealed three factors that impacted on the success of the patient engagement event: setting the stage, the power of storytelling, and the value of reframing the patient role. CONCLUSION: The present study highlights three components of patient and public engagement approaches - the importance of setting the proper stage, storytelling as a tool, and reframing the patient role in healthcare delivery - which may provide useful guidance to those considering similar patient and public engagement events.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".