Assessing Age-Friendly Community Initiatives: Developing a Novel Survey Tool for Assessment and Evaluation
Bibliographic record
Abstract
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 including during and post COVID Pandemic. 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 policymakers 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 | 0.079 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 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".