Do Interest Groups Cultivate Interest? Trajectories of Geriatric Interest Group Members
Bibliographic record
Abstract
BACKGROUND: Minimal exposure, misconceptions, and lack of interest have historically driven the shortage of health-care providers for older adults. This study aimed to determine how medical students' participation in the National Geriatrics Interest Group (NGIG) and local Geriatrics Interest Groups (GIGs) shapes their career development in the care of older adults. METHODS: An electronic survey consisting of quantitative and qualitative metrics to assess the influence of Interest Groups was distributed to all current and past members of local GIGs at Canadian universities since 2017, as well as current and past executives of the NGIG since 2011. Descriptive statistics and thematic analysis were performed. RESULTS: Thirty-one responses (27.7% response rate) were collected from medical students (13), residents (16), and physicians (2). 79% of resident respondents indicated they will likely have a geriatrics-focused medical practice. 45% of respondents indicated GIG/NGIG involvement facilitated the establishment of strong mentorship. Several themes emerged on how GIG/NGIG promoted interest in geriatrics: faculty mentorship, networking, dispelling stigma, and career advancement. CONCLUSION: The positive associations with the development of geriatrics-focused careers and mentorship compel ongoing support for these organizations as a strategy to increase the number of physicians in geriatrics-related practices.
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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 | 0.004 | 0.021 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".