ASSESSING AGE INCLUSIVITY USING THE AGE-FRIENDLY INVENTORY AND CAMPUS CLIMATE SURVEY
Bibliographic record
Abstract
Abstract During a time of increased focus on diversity, equity, and inclusion in institutions of higher education, age inclusion remains largely unexplored. The Age-Friendly Inventory and Campus Climate Survey (ICCS) is a tool that institutions of higher education can use to identify strengths and weaknesses specific to age-friendliness. This instrument measures age inclusivity by comparing age-friendly practices with student, staff, faculty, and lifelong learners’ perceptions of these practices. This symposium explores the process of using the ICCS at four different universities to understand the methods used to target each institution’s unique needs, with the goal of improving age-friendliness in higher education: 1) Zimmer et al., present the outcomes of using the ICCS to establish a baseline of age-friendliness at the University of Calgary and the process to generate action items from the results to develop a plan to enhance age-friendliness. 2) Waterhouse et al., describes the process of customizing the ICCS to meet the unique needs of an academic medical campus at the University of Colorado Anschutz Medical Campus. 3) Zisberg et al., report on the experience of translating the ICCS into Hebrew and the subsequent results implementing the tool at the University of Haifa. 4) Eaton & Friberg-Felsted explore the use of the ICCS within the College of Nursing at the University of Utah to identify ways to meet the learning needs of age diverse groups. Dr. Susan Whitbourne, from the team that developed the ICCS, will facilitate discussion surrounding age inclusivity in higher education.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".