System models for resilience in gerontology: application to the COVID-19 pandemic
Bibliographic record
Abstract
The care needs for aging adults are increasing burdens on health systems around the world. Efforts minimizing risk to improve quality of life and aging have proven moderately successful, but acute shocks and chronic stressors to an individual's systemic physical and cognitive functions may accelerate their inevitable degradations. A framework for resilience to the challenges associated with aging is required to complement on-going risk reduction policies, programs and interventions. Studies measuring resilience among the elderly at the individual level have not produced a standard methodology. Moreover, resilience measurements need to incorporate external structural and system-level factors that determine the resources that adults can access while recovering from aging-related adversities. We use the National Academies of Science conceptualization of resilience for natural disasters to frame resilience for aging adults. This enables development of a generalized theory of resilience for different individual and structural contexts and populations, including a specific application to the COVID-19 pandemic.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".