DEVELOPING THE INTERGENERATIONAL TECHNOLOGY ENGAGEMENT CHECKLIST FOR HIGHER EDUCATION (IG TECH)
Bibliographic record
Abstract
Abstract Intergenerational programs, as part of service learning initiatives in higher education, have shown to greatly benefit students in gaining professional experience that connects with classroom learning. In recent years, intergenerational technology programs in the United States and Canada have worked to enhance digital inclusion for older adults and provide service learning opportunities for students. Funded by the RRF Foundation for Aging, our multi-university research team set out to examine the implementation of programs that utilized the Cyber-Seniors reverse mentoring model within higher education. This presentation will describe the creation of a checklist to aid in the development of intergenerational technology programs within higher education, with the goal of building sustainable programs. The checklist was developed following a systematic review of the literature and interviews with 12 stakeholders. The systematic review included the analysis of 17 sources that addressed key elements of intergenerational programs such as planning, technology, accessibility, inclusivity, recruitment, marketing, safety, implementation, staffing, and training. Interviews were conducted with a purposive sample of ten higher education representatives and two Cyber-Seniors staff members, all with extensive knowledge of barriers and facilitators to program implementation. This resulted in the development of the InterGenerational Technology Engagement Checklist for Higher education (IG TECH). The IG TECH includes a series of questions to help faculty and/or staff make decisions about key elements of the program, including program planning, student recruitment, older adult recruitment and onboarding, and implementation/monitoring. Future research will evaluate the checklist through a Delphi Panel.
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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 | 0.065 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".