Incorporating Paid Caregivers Into Medical Education to Enhance Medical Student Exposure to This Essential Workforce
Bibliographic record
Abstract
The implications of the COVID-19 pandemic underscored the utility of home-based health care due in part to social distancing requirements, curtailment of elective hospital procedures, and patient apprehension of the health care setting. The pandemic particularly accentuated the integral role of paid caregivers (eg, home health aides, personal care attendants, and other home care workers) in caring for patients with chronic health conditions. Given the paradigm shift toward community- and value-based health care models, paid caregivers are likely to play an even greater role as care team members. Despite the increasingly prominent role paid caregivers are assuming in health care, especially for patients who are chronically ill, in our experience as medical students, we have very little exposure to these care team members, with most interactions occurring in brief, chance encounters. Specifically, we advocate for increased medical student exposure to paid caregivers to facilitate their recognition as valuable care team members. We propose to achieve this through (1) classroom-based module learning with live paid caregivers and (2) plain language communication training to enhance reciprocal engagement.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".