Prioritizing supports and services to help older adults age in place: A Delphi study comparing the perspectives of family/friend care partners and healthcare stakeholders
Bibliographic record
Abstract
BACKGROUND: Aging in place (AIP) is a policy strategy designed to help older adults remain in their community. While planners internationally have modified aspects of the older adult care continuum (e.g., home care, assisted living, nursing homes) to facilitate AIP, further improvements to community-based supports and services are also required. This study compared and constrasted the community-based factors (e.g., supports, services and personal strategies or characteristics) that family/friend care partners and healthcare stakeholders (i.e., planners/providers) view as most important to help older adults successfully AIP. METHODS: An initial list of factors shown to influence AIP was created from the academic literature. These factors were used to develop a Delphi survey implemented separately on care partners and healthcare stakeholders. Respondents rated the importance of each factor using a 10-point Likert Scale (1 = not important; 10 = absolutely critical). Consensus in each group was defined when at least 80% of participants scored a factor ≥8 ("very important"), with an interquartile range ≤2. Respondents suggested additional factors during Delphi round one. RESULTS: Care partners (N = 25) and healthcare stakeholders (N = 36) completed two and three Delphi rounds, respectively. These groups independently agreed that the following 3 (out of 27) factors were very important to help older adults age in place: keeping one's home safe, maintaining strong inter-personal relationships, and coordinating care across formal providers. While healthcare stakeholders did not reach consensus on other factors, care partners agreed that 7 additional factors (e.g., access to affordable housing, having mental health programs) were important for AIP. CONCLUSIONS: Compared to healthcare stakeholders, care partners felt that more and diverse community-based factors are important to support older adults to successfully AIP. Future research should replicate these findings in other jurisdictions, examine the availability and accessibility of the priority factors, and develop sustainable solutions to enhance their effectiveness.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".