What Kind of Doctor Do You Want to Be? Geriatric Medicine Podcast as a Career Planning Resource
Bibliographic record
Abstract
INTRODUCTION: For optimal direction in career paths and postgraduate training, students can benefit from information to guide them through options. Using geriatric medicine as a template, the goal was to develop a multimedia podcast resource that can give a clearer picture of what a specialty entails. METHODS: The project included a survey of existing resources and needs assessment of medical students at the University of Ottawa, Canada. This survey assessed students' knowledge of geriatrics and interest in the field and explored what they foresee as being important to be informed on when considering application to programs. Based on this, interview questions and content were developed for a podcast which was then evaluated. RESULTS: Interviews were conducted with physicians and residents nationwide. Relevant resources and links were added to the podcast. Evaluation demonstrated improved student understanding and interest in geriatric medicine as a career. Point-by-point format for a template on how to develop similar podcasts was developed to assist other specialties looking to develop similar information. CONCLUSIONS: As no such framework currently exists, results of this project can serve as a template for other postgraduate programs in developing a multimedia resource for informing prospective trainees.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".