THE EFFECTS OF WISDOM AND HEALTH ON NORTH AMERICAN SENIORS’ WELL-BEING SINCE COVID-19
Bibliographic record
Abstract
Abstract Public health restrictions necessitated by COVID-19 resulted in significantly reduced social contact for many older people, given their increased risks of infection and developing severe symptoms, even death. While reduced social contact can be a major cause of distress, our recent North American study (n=307) found 3 levels of resilience to the impact of COVID-19 – high (“well-adapted”), average (“getting-by”) and low (“struggling”) – associated with changes to wellbeing before (Time1), in summer 2020 (Time2), and about 18 months after (Time3). The present study investigates the wellbeing trajectory of 69 older individuals (Mage = 58.83, SDage = 7.21, max. = 77, min. = 50) within that larger sample who reported closely following local physical distancing recommendations. Specifically, it examines how their wellbeing was affected by their country of residence, and self-reported personal wisdom, self-transcendence, and health at Time2 and Time3. Simple logistic regression models suggest that, across Time2 and Time3, higher wisdom and better health were associated with higher likelihoods of being well-adapted vs. just getting-by. Higher self-transcendence at Time3 but not Time2 increases the likelihood of being well-adapted. Multiple logistic regressions with country, personal wisdom, self-transcendence, and health as predictors show that, controlling for all other variables in the model, higher wisdom and better health at Time2, as well as higher self-transcendence and better health at Time3, increase the likelihood of being well-adapted vs. just getting-by. Our findings demonstrate the protective values of personal wisdom, self-transcendence, and health during prolonged periods of isolation and stress for the older population.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".