Are We Afraid to Use Regulatory and Policy Levers More Aggressively to Optimize Patient Safety?
Bibliographic record
Abstract
COMMENTARY ContextHealthcare is a very labour-intensive process.The performance, individually and collectively, of a diverse array of healthcare workers has profound implications for the safety of care provided to patients and clients.It is worthwhile to consider how effectively we have used regulatory and policy levers over the past 10 years to assure optimally safe performance by the entire healthcare workforce.In any consideration of human performance, it is important to differentiate between human capacity to perform at a high level and the consistency of actual human actions on a day-today basis.It is important to remain ever mindful of the factors that influence performance capacity and those that influence workplace actions.In 1990, George Miller published in Academic Medicine, an article that described four facets of professional expertise and visually depicted these facets as layers of a pyramid (Miller 1990).In Miller's Pyramid, "knows" forms the base, followed sequentially by "knows how," "shows how" and "does."Although Miller applied this construct to professionals, I believe it is applicable to all workers.Patient safety is compromised when there is a gap between worker capacity to perform safely (know how) and actual worker performance (does).Both regulatory and policy levers can narrow that gap if they are applied effectively.Historically, we have applied regulatory and policy levers quite differently to professional workers as opposed to non-professional workers.We have also applied these levers differently to healthcare
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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 | 0.074 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.034 | 0.047 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.038 | 0.064 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".