A qualitative study of patients’ and caregivers’ perspectives on educating healthcare providers
Bibliographic record
Abstract
BACKGROUND: Patients/caregivers can be actively involved in the education of healthcare providers (HCPs). The purpose of this study was to explore patients'/caregivers' perspectives on their involvement and roles in the education of HCPs. METHODS: We invited patients/caregivers to participate in one-on-one semi-structured interviews. We analyzed the interview data using conventional content analysis to identify themes. RESULTS: In terms of patient/caregiver involvement in the education of HCPs, we identified that patients/caregivers perceive that it: (a) is challenging because of power-differentials between themselves and HCPs; (b) requires patient training; (c) needs to start early in HCPs' education processes; (d) can improve patient-HCP partnerships; and (e) requires compensation for patients. With regards to the roles that patients can play in educating HCPs, we found that patients/caregivers want to: (a) teach HCPs about patients' expectations, experiences and perspectives through case studies, storytelling, and educational research; (b) provide direct feedback to HCPs; and (c) advise on curricula development and admission boards for HCPs. CONCLUSIONS: Understanding patients'/caregivers' perspectives on this topic can help educational leaders and HCPs improve active patient/caregiver involvement in the education of HCPs. We need to listen to patients'/caregivers' voices in order to make effective changes in current and future health professions education.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".