Remediation in Practicing Physicians: Current and Alternative Conceptualizations
Bibliographic record
Abstract
Suboptimal performance in practicing physicians is a decades-old problem. The lack of a universally accepted definition of remediation, the paucity of research on best remediation practices, and the ongoing controversy regarding the institutional responsibility for enacting and overseeing the remediation of physicians suggest that it is not merely a difficult problem to solve but a problem that the community does not grapple with meaningfully. Undoubtedly, logistical and political considerations contribute to this state of affairs; however, other underlying conceptual issues may also play a role in the medical profession's difficulties in engaging with the challenges around remediation.Through a review of the medical education and other literatures, the authors examined current conceptualizations of both reme-diation itself and the individual being remediated, as well as how the culture of medicine influences these conceptions. The authors explored how conceptualizations of remediation and the surrounding culture might affect not only the medical community's ability to support but also its willingness to engage with physicians in need of remediation.Viewing remediation as a means of supporting practice change-rather than as a means of redressing gaps in knowledge and skill-might be a useful alternative conceptualization, providing a good place to start exploring new avenues of research. However, moving forward will require more than simply a reconceptualization of remediation; it will also necessitate a change in how the community views its struggling members and a change in the medical culture that currently positions professional autonomy as the foundational premise for individual practice improvement.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.006 | 0.083 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".