Characteristics, Predictors and Reasons for Regulatory Body Disciplinary Action in Health Care: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT What research has been done to characterize the outcomes of disciplinary action or fitness-to-practice cases for regulated health professionals? To answer this research question, relevant publications were identified in PubMed, Ovid EMBASE, CINAHL via EBSCOhost, and Scopus. Included papers focused on reviews of regulatory body disciplinary action for regulated health professionals. Of 108 papers that were included, 84 studied reasons for discipline, 68 studied penalties applied, and 89 studied characteristics/predictors of discipline. Most were observational studies that used administrative data such as regulatory body discipline cases. Studies were published between 1990–2020, with two-thirds published from 2010–2020. Most research has focused on physicians (64%), nurses (10%), multiple health professionals (8.3%), dentists (6.5%) and pharmacists (5.5%). Most research has originated from the United States (53%), United Kingdom (16%), Australia (9.2%), and Canada (6.5%). Characteristics that were reviewed included: gender, age, years in practice, practice specialty, license type/profession, previous disciplinary action, board certification, and performance on licensing examinations. As most research has focused on physicians and has originated from the United States, more research on other professions and jurisdictions is needed. Lack of standardization in disciplinary processes and definitions used to categorize reasons for discipline is a barrier to comparison across jurisdictions and professions. Future research on characteristics and predictors should be used to improve equity, support practitioners, and decrease disciplinary action.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it