Characteristics, Predictors and Reasons for Regulatory Body Disciplinary Action in Health Care: A Scoping Review
Bibliographic record
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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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 | 0.035 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.022 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".