The need for needs assessment in continuing medical education
Bibliographic record
Abstract
Maintenance of professional competence is a critical component of professionalism. However, traditional methods, which rely on individual self assessment, are inadequate. Conversely, legislated recertification programmes are difficult to individualise and can be perceived as draconian. What is required are better methods of standardised individual needs assessment. We suggest some possible strategies. Like all professions medicine is granted professional autonomy by society under the assumption that its practitioners will be deemed competent on entry into practice and will maintain competence for as long as they practise. Traditionally it is the responsibility of the individual practitioner to do whatever is necessary to remain competent. In the past maintaining one's competence was not problematic because relevant knowledge accreted slowly. Today, however, without a programme of active learning no doctor can hope to remain competent for more than a few years after graduation. One response to this challenge has been for education programmes, particularly problem based ones such as our own, to focus on the development of self assessment skills and self directed learning skills in order to equip graduates to maintain competence. The evidence, however, while not abundant, shows that this was a quixotic quest. The evidence that graduates from problem based learning are better at “keeping up” is weak.1 2 Moreover, many studies have shown that self assessment is far more difficult than we thought.3 Finally, self assessment does not emerge on graduation as a consequence of the demands of changing practice. Sibley et al observed that practitioners tend to pursue education around topics they are already good at while avoiding areas in which they are deficient and where there may be room for improvement.4 The evidence shows therefore that self monitoring programmes such as the maintenance of competence (MOCOMP) programme,5 which leave practitioners to their own devices, …
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".