Exploring the potential of decision aids to support decision-making about healthy aging: A qualitative descriptive study using the theoretical domains framework
Bibliographic record
Abstract
OBJECTIVES: To explore how older adults and caregivers perceive the potential of seven decision aids (DAs) to support decision-making in adopting new lifestyles for healthy aging. METHODS: We developed seven DAs to support lifestyle decision-making among older adults. In this qualitative-descriptive study, we recruited French-speaking adults aged 65 + and caregivers spending at least one hour weekly with an older adult, through primary care clinics and online. Participants read one of the DAs, and we then conducted semi-structured interviews to explore their views on the strengths and limitations of DAs in supporting lifestyle decision-making. Transcribed discussions were thematically analyzed by two researchers deductively using the Theoretical Domains Framework, and then inductively. RESULTS: We recruited 21 older adults (77.2 years ± 5.3, 12 women) self-reporting good/excellent health, and 14 caregivers (52.4 years ± 9.2). Results suggest that the studied DAs provide a holistic view of health multiple dimensions, helping older adults clarify their health priorities and supporting decision-making. Contents on social influences, particularly peer influences, emerged as a key for decision-making about adopting a new healthy aging lifestyle. The DAs' focus on recognizing experiential knowledge and providing a personalized approach to health education emerged as key strength. Our findings indicate that health promotion DAs can also be used to prepare older adults for future shared decision-making processes. CONCLUSION: DAs can help older adults integrate their experiential knowledge and identify strengths to engage in informed lifestyle decision-making. PRACTICE IMPLICATION: DAs offer a practical approach to tailoring evidence-based information on lifestyle changes to support decision-making about healthy aging.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".