Lessons Learned From a Canadian Province-Wide Age-Friendly Initiative: The Age-Friendly Manitoba Initiative
Bibliographic record
Abstract
The Age-Friendly Manitoba Initiative was launched in 2008. A formative evaluation we conducted in 2011 with 44 participating rural and urban communities demonstrates considerable progress, with virtually all communities having formed an Age-Friendly Committee and conducting a community assessment to identify priorities for action. The majority of communities implemented one or more age-friendly projects. Major barriers to becoming age-friendly identified by participants included lack of funding; lack of capacity, particularly in small communities; and lack of leadership or direction. The study highlights the importance of strong leadership at all levels of government (municipal, provincial, federal); the need to support communities, particularly rural ones, as they try to become more age-friendly; and the importance of ongoing promotion of age-friendliness locally and more broadly (e.g., provincially).
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".