Evaluating a Geriatric Educational Program: Exploring Opportunities for Increasing Impact and Scale
Bibliographic record
Abstract
Abstract The geriatric population is rapidly growing, and this growth is beyond the pace of increase in the number of healthcare professionals who are qualified to care for and tend to the various needs of this significant subgroup of the population. The current university curricula have not been sufficient in terms of quantity as well as their ability to address the ageism inherent in the perspectives of students from across the educational spectrum. In recognition of the absence of standardized geriatric guidelines, medical associations across Canada and the United States have established geriatric learning competencies for medical programs. Nevertheless, there are exiting gaps regarding the development and evaluation of geriatric-focused didactic programs that adequately train and build competency among the students interested in pursuing careers with geriatric-specific elements. A university-wide program was developed to enhance aging education and build competency through sparking interest, providing better education related to aging, and building better relationships between future healthcare professionals and older adults. To evaluate the impact of this program, a logical framework was developed a-priori and revised through constant iterations and following discussion with the program’s multidisciplinary stakeholder group. Quantitative measures are being augmented with in-depth qualitative interviews to explore elements influencing students’ experiences with the program and the effect on their interests in and attitudes towards geriatrics. The results will inform our conclusions regarding program effectiveness in enhancing interest in geriatric-focused education among the students and trainees and assist with recommending future directions regarding impact and large-scale dissemination and implementation.
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 | 0.094 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".