WORKING WITH DIVERSE ELDERS IN RURAL COMMUNITIES (OPPORTUNITIES AND STRENGTHS)
Bibliographic record
Abstract
Abstract The focus of this symposium is to discuss the opportunities and strengths of working with diverse elders and older adults in rural communities. Accounts of field experiences will be shared, and methods of utilizing existing data that hold promise for addressing current problems; along with building the foundation of communication and relationship requisite to successful design, development, and implementation of improvements will be described through various studies with rural and/or indigenous peoples. We will discuss the role of social determinants of health, race, and rurality on frailty trajectories utilizing data from the National Health and Aging Trend Study. An innovative approach for leveraging older adult farmers’ expertise to facilitate effective emergency decision-making in Fraiser Valley, British Columbia, Canada, will be described. Ongoing research with Alaskan Native Elders provides insight into the need for effective research with Indigenous cultures, especially Elders, by developing trusting and mutually beneficial relationships. Research strategies involved historical, social, cultural, and inter-relational perspectives. Insight into how Cuidando con Respeto, a program with Latinx elders, builds bridges to health care and mutual understanding nurtured at the community level and shows promise in empowering caregivers within a familismo-culturally relevant manner will be shared. The use of mobile pain applications and considerations when designing health technology will be examined with diverse older adults.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".