National programmes for validating physician competence and fitness for practice: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To explore and categorise the state of existing literature for national programmes designed to affirm or establish the continuing competence of physicians. DESIGN: Scoping review. DATA SOURCES: MEDLINE, ERIC, Sociological Abstracts, web/grey literature (2000-2014). SELECTION: Included when a record described a (1) national-level physician validation system, (2) recognised as a system for affirming competence and (3) reported relevant data. DATA EXTRACTION: Using bibliographic software, title and abstracts were reviewed using an assessment matrix to ensure duplicate, paired screening. Dyads included both a methodologist and content expert on each assessment, reflective of evidence-informed best practices to decrease errors. RESULTS: 45 reports were included. Publication dates ranged from 2002 to 2014 with the majority of publications occurring in the previous six years (n=35). Country of origin--defined as that of the primary author--included the USA (N=32), the UK (N=8), Canada (N=3), Kuwait (N=1) and Australia (N=1). Three broad themes emerged from this heterogeneous data set: contemporary national programmes, contextual factors and terminological consistency. Four national physician validation systems emerged from the data: the American Board of Medical Specialties Maintenance of Certification Program, the Federation of State Medical Boards Maintenance of Licensure Program, the Canadian Revalidation Program and the UK Revalidation Program. Three contextual factors emerged as stimuli for the implementation of national validation systems: medical regulation, quality of care and professional competence. Finally, great variation among the definitions of key terms was identified. CONCLUSIONS: There is an emerging literature focusing on national physician validation systems. Four major systems have been implemented in recent years and it is anticipated that more will follow. Much of this work is descriptive, and gaps exist for the extent to which systems build on current evidence or theory. Terminology is highly variable across programmes for validating physician competence and fitness for practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.097 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.033 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 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".