Ask the Parent: Developing a Pediatric Feedback Form for Medical Learners
Bibliographic record
Abstract
BACKGROUND: Patient-centered medicine prioritizes patients' perspective to actively involve them in their care. Medical education and assessments must reflect this approach. Current patient feedback forms for medical learners are designed for the adult patient and are thus not suited for pediatrics. We aimed to determine if parents/caregivers and simulated patients (SPs) are willing to provide feedback to medical learners and if learners would be receptive to this feedback. We then identified which specific feedback caregivers, SPs, and learners consider most important. METHODS: REDCap surveys were emailed to caregivers whose child had been seen at Child and Youth Services in Regina, Saskatchewan, from 2020 to June 2023, and to University of Saskatchewan (USask) SPs. Another survey was sent to USask medical students and family medicine and pediatric residents. Surveys asked what specific feedback each group would most prefer to give (caregivers/SPs) or receive (learners) using a Likert scale to rate importance. Descriptive statistics were computed using R software. The highest-ranked options were combined to form a single questionnaire to be given following a clinical encounter. RESULTS: All three groups agree that medical learners should receive feedback from sources beyond physicians alone (caregivers: 73.6%, SPs: 89.5%, and learners: 88.9%). The five most highly rated areas for feedback were "explains things clearly," "involves me in the decisions about the medical plans (for my child)," "addresses my concerns and takes them seriously," "listens and gives their full attention," and "did or said anything that made me (or my child) uncomfortable." CONCLUSIONS: All three groups overwhelmingly agree that caregivers/patients should provide feedback on learners' clinical skills, confirming the utility of a pediatric feedback form. The most important areas of feedback identified were consolidated into a user-friendly feedback form consisting of five questions with a Likert-scale rating plus a section for free written narrative feedback.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".