An environmental scan of methods for assessing age-friendliness in post-secondary institutions
Bibliographic record
Abstract
The University of Calgary joined the Age-Friendly University (AFU) Global Network in 2018. As part of our university's AFU action plan, a baseline assessment of the institution's age-friendliness will be conducted to identify areas of strength and growth. To inform our approach and that of other institutions undertaking this work, an environmental scan was performed to determine methods used by post-secondary institutions to date to assess age-friendliness. Both academic and grey literature published between 2012 and 2022 in the English language was searched using diverse keywords. The academic literature was identified from four databases (Abstracts in Social Gerontology, Academic Search Complete, Education Research Complete, Scopus) and the grey literature from 84 institutional websites of AFU Global Network members. Twelve academic sources and four grey sources were included in the analysis. Seven methods were used to assess age-friendliness, with the most common approaches being surveys, inventories, focus groups, interviews, and photovoice. The Age-Friendly Inventory and Campus Climate Survey (Silverstein et al., 2022) was selected to evaluate the University of Calgary's age-friendliness due to its alignment with all 10 AFU principles, comprehensiveness, and involvement of multiple stakeholders. Other post-secondary institutions should consider their context and resources when selecting an assessment method.
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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 | 0.064 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.049 | 0.052 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".