Recruiting and Training a Health Professions Workforce to Meet the Needs of Tomorrow’s Health Care System
Bibliographic record
Abstract
The quality of any health care system depends on the caliber, enthusiasm, and diversity of the workforce. Yet, workforce research often focuses on the number and type of health professionals needed and anticipated shortages compared with anticipated needs. These projections do not address whether the workforce will have the requisite social, intellectual, cultural, and emotional capital needed to deliver care in an increasingly complex health care system.Building a workforce that can deliver care in such a system begins by recruiting individuals with the requisite knowledge, skills, and attributes. To address this and other workforce needs, the authors argue that health professions education programs must make purposeful changes to their admissions criteria, such as focusing on emotional intelligence and diversity and recruiting students from the communities where they will return to work; partner with communities; ensure that accreditation systems support these goals of fostering diversity; recruit students who can bridge the gap between public health and health care; and invest in health professions education research.In this article, they contemplate how health professions education programs can recruit and educate talented health professionals to create a high-performing workforce that is capable of serving in the complex health care system of tomorrow. They provide examples of successful programs to highlight the potential effects of their recommendations.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".