Optimizing Older Adult Mental Health in Support of Healthy Ageing: A Pluralistic Framework to Inform Transformative Change across Community and Healthcare Domains
Bibliographic record
Abstract
This paper describes a pluralistic framework to inform transformative change across community and healthcare domains to optimize the mental health of older adults in support of healthy ageing. An extensive review and analysis of the literature informed the creation of a framework that contextualizes the priority areas of the WHO Decade of Health Ageing (ageism, age-friendly environments, long-term care, and integrated care) with respect to older adult mental health. The framework additionally identifies barriers, facilitators, and strategies for action at macro (social/system), meso (services/supports), and micro (older adults) levels of influence. This conceptual (analytical) framework is intended as a tool to inform planning and decision-making across policy, practice, education and training, research, and knowledge mobilization arenas. The framework described in this paper can be used by countries around the globe to build evidence, set priorities, and scale up promising practices (both nationally and sub-nationally) to optimize the mental health and healthy ageing trajectories of older adults as a population.
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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 | 0.042 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.006 | 0.008 |
| 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".