An Examination of Best Practice in Multi-Service Senior Centres
Bibliographic record
Abstract
Since 1976, the Kerby Centre has provided a one-stop-shop for educational, social, wellness, and recreational services and supports for Calgary’s seniors, with the vision of “a happy, healthy senior population.” With plans to relocate its programs and services to a new facility to better serve older Calgarians, the Kerby Centre sought information regarding best practice models in multi-service senior centres. The Kerby Centre contracted the Canadian Research Institute for Law and the Family to conduct a best practice literature review and environmental scan of best practice models for multipurpose senior centres. It is expected that this report will aid the Kerby Centre in future planning with regard to the new facility. The purpose of this project was to examine emerging trends and best practices (e.g., commonly implemented and/or innovative practices) for multi-purpose senior centres in other jurisdictions. Specifically, this project had the following objectives: (1) To determine key facility/amenity components for an ideal multi-purpose senior centre; (2) To determine key programs for an ideal multi-purpose senior centre; (3) Recommend strategic partnerships that could better position senior centres for success; and (4) Develop five to seven profiles of leading-edge multi-purpose senior centres as recommended targets for further investigation.
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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 | 0.076 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".