GeroCast: Using podcasting to deliver living cases in gerontology education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: To describe the creation of an educational podcast with ‘living cases’ of older adults to support students’ learning on a gerontology course and report on students’ evaluation of the project. Setting: Gerontology course in a graduate programme. Method: We developed a podcast series based on interviews with older adults in the community following recent guidelines for creating educational podcasts. The podcast episodes were used in a case-based group assignment to work on during the course and to present findings at the end. Evaluation: Student experiences were evaluated using a mixed-methods survey. Results: From November 2019 to January 2021, case-based podcasts, averaging 17 minutes in length, were created and evaluated. Most students found the content of the podcasts relevant to working with older adults and increased their understanding of the issues facing members of that population. Qualitative analysis of the survey findings found that the overall strengths of the podcasts were that they were well structured, provided an authentic, real-world experience, allowed listeners to experience an innovative teaching strategy, promoted reflection, and encouraged students to consider a future career working with older adults. Students also recommended ways to improve the podcasts. Conclusion: Delivering living case studies using podcasts is a feasible, inexpensive and effective teaching method for improving physiotherapy students’ attitudes towards caring for older adults. Students enjoyed learning via the podcasts and found them a valuable way to better understand the issues facing older adults. The living case podcasts could have broad applicability to other aging and health courses.
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.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it