A Unified Model of Resilience and Aging: Applications to COVID-19
Bibliographic record
Abstract
Drawing on multidisciplinary research focusing on a spectrum ranging from individual experience to structural system-level risk response and resilience, this article develops a rationale for a Unified Model of Resilience and Aging (UMRA). In response to a broad range of adversities associated with aging, it details the ways in which some individuals are able to bounce back better than others, or adapt better than expected, termed resilience. However, resilience and aging theoretical models have developed out of different disciplinary developments, ranging from individual levels to structural level complex systems, including several gerontological theoretical models addressing adaptation to life course and aging processes. The article reviews and synthesizes prior conceptual and theoretical work, and their empirical groundings, in order to develop an integrated resilience model with wide applications to aging-related problems including chronic illness, mental health, widowhood, poverty, caregiving burden, etc. The article focuses specifically on COVID-19 pandemic risk, response and resilience in order to specify applications of the UMRA, and to suggest avenues for future research and testing of theoretical axioms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".