P.101 Case-oriented needs assessment for professional development in an academic neurology centre
Bibliographic record
Abstract
Background: Needs assessment is a cornerstone of designing programs for continuing professional development (CPD). However, typical needs assessment surveys often yield non-specific information insufficient to guide professional development programming decisions. Methods: A survey was distributed to Neurologists practicing in city of Calgary. A stimulated-recall method was used to generate specific case-oriented clinical questions and 5-point Likert scales were used to rate specific topics across the CanMEDS competency framework and CPD preferences. Results: A total of 48 surveys were distributed, with a response rate of 62.5%. Most respondents were subspecialists in Neurology (87%) in practice for less than 15 years (71%). Most used local neuroscience (97%) rounds as source for CPD. Respondents reported a need to address specific questions relating to the following topics: Acute stroke (54%), non-acute stroke (45%) and epilepsy (50%). For example, physicians identified that they wanted to learn more about when to reinitiate anticoagulation following ischemic stroke, or which choice of anti-epileptic for various seizure presentations. Specific medical content was rated highly disproportionately to other physician competencies such as communication or management skills. Conclusions: Our survey elicited detailed learning gaps from academic neurologists and identified a disconnect in interest in topics related to medical content compared to other important physician competencies.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".