Getting back on track: a systematic review of the outcomes of remediation and rehabilitation programmes for healthcare professionals with performance concerns
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the evidence regarding outcomes of remediation and rehabilitation programmes for healthcare professionals with performance concerns, and to explore if outcomes differ for specific concerns and professions. METHODS: A search in four databases (Medline, Embase, PsycINFO and CINAHL) was conducted from 1 January 1990 to 7 May 2017. Studies reporting on outcomes of nationwide and state-wide programmes aimed at remediation and rehabilitating healthcare professionals with performance concerns (ie, dentists, midwives, nurses, pharmacists, physicians, physiotherapists, psychologists and psychotherapists) were included. RESULTS: We included a total of 38 studies. More than half of the studies included programmes in the USA (57.9%), and a majority of studies focused on outcomes for physicians (78.9%) and on outcomes for substance use disorders (SUDs, 63.2%). Programme completion rates for SUDs were positive and approximately 80%-90% of participants were employed after treatment. Studies that reported on remediation outcomes for dyscompetence, almost all from Canada (7/8), showed varying results. One study compared outcomes for performance concerns in the same programme (ie, SUD and other mental and behavioural problems) and showed comparably successful results. No study specifically compared outcomes between professions. CONCLUSION: The literature is dominated by outcomes for physicians in North American programmes, with positive outcomes for SUD and varying outcomes for dyscompetence. Based on our findings we cannot make valid comparisons in outcomes between professions and specific performance concerns, and we call for other programmes to report on outcomes for different professions and concerns. Because of the positive outcomes of physician health programmes, other countries should consider introducing similar programmes to support healthcare professionals getting back on track.
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 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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".