Clinical Neuroscience Podcasts for Family Medicine, Internal Medicine, and Neurology Residents: A Needs Assessment Study (P1.312)
Bibliographic record
Abstract
OBJECTIVE: The aim of the current study was to identify the preferred content and format of a proposed series of clinical neuroscience podcasts using a survey-based needs assessment of Family Medicine, Internal Medicine, and Neurology residents. BACKGROUND: Podcasts are a form of audio media that have increasingly been adopted to enhance medical education. Unanswered questions include whether podcasts could serve as an effective tool to fill knowledge gaps identified a priori by learners or which production features would make learners more likely to use the podcasts. METHODS: 77 Family Medicine, 19 Internal Medicine, and 14 Neurology residents completed surveys to determine which topics and production features they deemed to be most important. Responses were analyzed both quantitively (e.g. Likert scales rating production features) and quantitatively (e.g. using coding techniques to identify topics of interest on free-form responses). RESULTS: Among all the responders, 93% had listened to autio from the internet in the past year, 74% had listened to a podcast, and 64% had listened specifically to a medical education podcast. The top 3 podcast production features as rated on a 5 point Likert scale were: (mean, 95% confidence interval): credibility of podcast source material (4.24, 4.10-4.37); ability to navigate quickly to desired content/excerpts (4.19, 4.06-4.31), and; production and audio quality (4.09, 3.98-4.20). The most common content themes identified by Family Medicine residents were headache, multiple sclerosis, stroke, stroke rehabilitation, and degenerative disc disease. Internal Medicine residents most frequently identified stroke, peripheral neuropathy, neurological exam, CNS tumours, and stroke rehabilitation. Neurology residents most frequently identified stroke, mulitple sclerosis, epilepsy, management of raised intracranial pressure, and spasticity management. CONCLUSIONS: There was much interest in clinical neuroscience podcasts among residents in Family Medicine, Internal Medicine, and Neurology. We identified features and content that can be incorporated into our proposed podcasts to specifically address the needs of our potential audience.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".