IMPROVING ELDER HEALTH BEHAVIORS AND STUDENT LEARNING OUTCOMES WITH HOPE-BASED INTERPROFESSIONAL EDUCATION PROGRAM
Bibliographic record
Abstract
Abstract Although Interprofessional Education (IPE) and research enhances learning, communication, and care of patients, it remains underutilized in higher education, demonstrating the need for extracurricular IPE opportunities. This presentation describes an interprofessional research project that brought together faculty, undergraduate, and graduate students from several health and social science disciplines to design and deliver a 15-week healthy aging program for older adults living in the urban Circumpolar North. Five faculty and one graduate research assistant led the project while eight students team-taught weekly, 1-hour sessions in the community focusing on healthy lifestyles within a framework of Persuasive Hope Theory. We will present the results of the student satisfaction survey regarding their involvement with the research as well as the participant satisfaction with the program. Students reported gains in “thinking like a scientist,” increased confidence conducting research tasks, benefits from teamwork, and greater consideration of the needs of older adults in their field of study. Older adult participants (N=39) also indicated favorable opinions of the program, responding that they were satisfied with the student-instructors, learning, application, impact, and value of the program. Despite small sample sizes, results from student and participant surveys both demonstrate that the IPE and intergenerational learning opportunities were meaningful. Additionally, results suggest that students may be more likely to consider a career working with older adults if given hands-on research experiences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".