Six ways to get a grip on patient and family centered care during the undergraduate medical years
Bibliographic record
Abstract
Patient and family-centered care and patient engagement practices have strong evidence-based links with quality and safety for both patients and health care providers. Expectations for patient and family-centered care have advanced beyond hearing the patient perspective and taking patient wishes into account. A participatory approach including patients as partners in their care journey is expected, but attitudes toward patient and family-centered care remain barriers in practice. As health service organizations shift from a system-centered approach to a patient and family-centered care delivery model, black ice occurs. In this Black Ice article, we present some practical tips for medical educators to improve opportunities for medical students to develop knowledge, attitudes, and skills that support patient and family-centered care.
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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 | 0.043 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.011 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".