Competence and Cognitive Difficulty in Physicians: A Follow-up Study
Bibliographic record
Abstract
PURPOSE: Remediation of incompetent physicians has proven difficult and sometimes impossible. The authors wished to determine whether such physicians had neuropsychological impairment sufficient to explain their incompetence and their failure to improve after remedial continuing medical education (CME). METHOD: Between 1997 and 2001, the authors undertook neuropsychological screening of 45 participants of a physician competency assessment program. For those physicians reassessed after a period of remediation, the authors relate the findings of the physicians' competence reassessments to their neuropsychological scores. RESULTS: Nearly all physicians performing well on competency assessment had no or mild cognitive impairment. Conversely, a significant number of physicians performing poorly on competency assessment had sufficient neuropsychological difficulty to explain their poor performance. The cognitive impairment was more marked in elderly physicians, and referencing the neuropsychological scores to an age-matched normative population underestimates the impairment. No physician with moderate or severe neuropsychological dysfunction had successful competency reassessment. Increasing age was associated with poor performance on competency testing, but was less strongly associated with unsuccessful reassessment. CONCLUSION: A large minority of the physicians who fell significantly below desired levels of competence had cognitive impairment sufficient to explain their lack of competence and their failure to improve with remedial CME.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".