Using the age-friendly inventory and campus climate survey at a Canadian university: process and outcomes
Bibliographic record
Abstract
The Age-Friendly Inventory and Campus Climate Survey (ICCS) is the most comprehensive instrument available to empirically examine age inclusivity in higher education. While widely used in the United States, it had not been used in Canada. The purpose of this article is to share our experience and outcomes from using the ICCS at a Canadian post-secondary institution - the University of Calgary. The inventory was completed by 10 administrators to determine the presence of age-friendly campus practices and environmental features at the university. The survey was completed by 178 faculty, 608 staff, and 1,167 students to understand their awareness and perceptions of age-friendly practices and features covered by the inventory. We found that the ICCS was transferrable to our national and institutional context with minor modifications. Some challenges were experienced in the administration of the instrument, particularly the survey due to administrative complexities in conducting a survey at a large institution. The results of the assessment indicated that our university is moderately age-friendly, but most survey participants were unaware of its age-friendly elements. The findings from this baseline assessment provided valuable insights that will inform the development of an action plan to enhance the University of Calgary's age-friendliness.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".