USING THE AGE-FRIENDLY INVENTORY AND CAMPUS CLIMATE SURVEY AT A MAJOR CANADIAN UNIVERSITY
Bibliographic record
Abstract
Abstract Since the University of Calgary became a member of the AFU Global Network, establishing a benchmark of the institution’s age-friendliness has been a priority for the Brenda Strafford Centre on Aging. The Age-Friendly Inventory and Campus Climate Survey was selected for our baseline assessment because it captures both objective and subjective elements of age-friendliness that align with the AFU principles. Prior to data collection, the instrument was modified for our national and institutional context by adding, removing, and changing the language of some items. The Age-Friendly Inventory was completed by administrators to determine the current status of our campus practices and environmental features, while the Campus Climate Survey was completed by faculty, staff, and students to understand their awareness and perceptions of these practices and features. A total of 10 administrators, 178 faculty, 608 staff, and 1167 students participated in the assessment. The results indicated that our university is moderately age-friendly, but participants were generally unaware of its age-friendly elements. To generate action items from the results, we analyzed the data further. Each inventory and survey item was mapped onto the AFU principle it most closely aligned with and then grouped with similar items to form categories for each principle. Through this process we identified areas of strength and growth for each principle, and prioritized principles (1, 4, 6, and 10) for our Centre to address. The findings from this study will inform an action plan to raise awareness of and 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".