Assessing Age-Friendly Community Initiatives: Developing a Novel Survey Tool for Assessment and Evaluation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Age-friendly community initiatives (AFCIs) have gained recognition as essential responses to the needs of aging populations. Despite their growing significance, there is a notable lack of effective measurement tools to assess the planning, implementation, and sustainability of AFCIs. The purpose of this study was to develop and validate a survey tool for evaluating AFCIs. RESEARCH DESIGN AND METHODS: A sequential exploratory mixed-method design was used in 2 phases. First, we identified key themes from interviews with AFCI leads to generate AFCI survey items and regional workshops. Then, we conducted a pilot of the survey and assessed its measurement properties. RESULTS: Thematic analysis of interviews with 68 key informants from 58 AFCIs revealed 4 main themes: AFCI priorities, enablers, challenges, and benefits. These themes, combined with feedback from AFCI stakeholders at the regional workshops and an AFCI conference, informed the development and refinement of a reliable and valid AFCI survey in 2019, supported by a high Cronbach's alpha value (α = 0.881). Steps were identified to maintain and sustain the AFCI survey over time. DISCUSSION AND IMPLICATIONS: The survey accommodates AFCIs' diverse demographics, governance structures, and priorities with a standardized and flexible approach for effective measurement. This research contributes to the academic understanding of AFCIs and aids community leaders and policy-makers in planning, implementing, and evaluating AFCIs.
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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 | 0.070 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".