Aging in Place and ‘The Little Things’: Prioritizing Mobile Health and Social Care in Rural Communities
Bibliographic record
Abstract
Older adults are more frequently wanting to age in place. Governments are seeking cost-effective and efficient methods of supporting aging populations. Older adults who want to stay in their homes for as long as possible encounter multiple barriers, including struggling to maintain their homes, inadequate levels of social and healthcare support, and the lack of financial capacity to pay for home support services. The Mobile Seniors' Wellness Network (MSWN), a multi-disciplinary and person-centered mobile health and social support intervention study was designed to investigate and support aging in place for older adults living in rural New Brunswick, Canada. Secondary analysis of case notes and exit interviews using content analysis revealed concerns with the lack of affordable and mobile care services for vulnerable rural older adults. Older adults revealed that their needs include "the little things" rather than grand gestures or sweeping policies to age in place such as assistance with grounds and home maintenance, in addition to relational and person-centered health and social care in the home. Reliance on private service delivery and volunteer organizations can increase the likelihood that older adults will experience a breakdown of social support networks tied together loosely by friends, family, and their communities. When services are unattainable aging in place becomes an unreachable goal.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".