Remediation Programs for Regulated Health Care Professionals: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Clinical competence is essential for providing safe, competent care and is regularly assessed to ensure health care practitioners maintain competence. When deficiencies in competence are identified, practitioners may undergo remediation. However, there is limited evidence regarding the effectiveness of remediation programs. The purpose of this review is to examine the purpose, format, and outcomes of remediation programs for regulated health care practitioners. METHODS: All six stages of the scoping review process as recommended by Levac et al were undertaken. A search was conducted within MEDLINE, Embase, CINAHL, ERIC, gray literature databases, and websites of Canadian provincial regulatory bodies. Emails were sent to Registrars of Canadian regulatory bodies to supplement data gathered from their websites. RESULTS: A total of 14 programs were identified, primarily for physicians (n = 8). Reasons for remediation varied widely, with some programs identifying multiple reasons for referral such as deficiencies in recordkeeping (n = 7) and clinical skills (n = 6). Most programs (n = 9) were individualized to address specific deficiencies in competence. The process of remediation followed three stages: (1) assessment, (2) active remediation, and (3) reassessment. Most programs (n = 12) reported that remediation was effective in improving competence. CONCLUSIONS: Regulatory bodies should consider implementing individualized remediation programs to ensure that clinicians' deficiencies in competence are addressed effectively. Further research is indicated, using reliable and valid outcome measures to assess competence immediately after remediation programs and beyond.
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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 | 0.032 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".