Training the Next Generation of Healthcare Providers to Address Overuse and Avoid Low‐Value Care
Bibliographic record
Abstract
In the previous chapter, we learned about the importance of sustaining and spreading successful de-implementation interventions. One key enabler for this is investment in the next generations of healthcare professionals. Practice patterns relating to low-value care amongst practicing clinicians are derived from training experiences and shaped by clinical learning environments. Several studies demonstrate that physicians who trained in high resource utilisation clinical settings were much more likely to themselves be high users of healthcare resources. Thus, efforts to work upstream and engage clinicians at their earliest stages of training is a critical driver of change to realise a culture of high-value care. This chapter focuses on the changes needed to leverage health professional education to advance high-value care competencies. We will provide an overview of competencies needed to deliver high-value care, summarise the various approaches taken to introduce high-value care concepts formally into training programme curricula, and highlight the importance of addressing elements of the clinical learning environment with a particular focus on faculty role modelling. We will also describe assessment strategies that can support efforts to evaluate learning outcomes related to high-value care. While much of the published literature cited in this chapter is specific to training physicians, the overarching principles and examples have relevance for all health professions' education related to overuse and high-value 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".