California's Master Plan for Aging, Health Reimagined: A Case for Seniors to Age-In-Place
Bibliographic record
Abstract
Abstract In 2021 Worldwide, communities face a singular yet common challenge; and this is the significant aging of their senior adult populations. Current evidence from the literature suggests that older adults prefer to stay in their homes as they age. However, the facilitators and challenges older people encounter in realizing their aging goals have been inadequately addressed by the current body of literature given the projected increase in the number of older people in the United States preferring to age from their homes. Aging adults are vulnerable to daily frustration, which could negatively impact their aging process. However, knowledge of traditional medical services, socio-ecological factors, and support services needed to facilitate their aging process in the home remain limited. The United States is projected to have their senior population outpace that of its children’s population, thus creating the need for increased and well-defined programs and services that support seniors to age-in-place. California’s over-60 population is growing faster than any other age group, and is projected by the year 2030, to include a quarter of its residents (10.8 million) as older adults. California’s rapidly changing and aging adult population increases the need for honoring the preference of older adults, who surveyed worldwide, 80% consistently wish to age-in-place, but face potential risk factors such as lack of health care access, chronic illness, clinical risk factors, socio-ecological risk factors, and socio-demographic risk factors. Through the literature, I learned that existing models of senior support programs and services, including the newly released January 2021 California’s Master Plan for Aging, which can positively aid California seniors with aging-in-place, implicating possible areas for further improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".