OLDER ADULTS' WORK DISRUPTIONS IN APRIL/MAY 2020: IMPLICATIONS FOR WORK STATUS AND MENTAL HEALTH OVER 6 MONTHS
Bibliographic record
Abstract
Abstract Using the COVID-19 Coping Study, we sought to determine how work disruptions for older adults in April/May 2020 related to labor force status in September/October 2020 and mental health throughout those six months (N=2,367). One-third of respondents who lost their job in April/May were unemployed at the end of follow-up, while 15% were unemployed after furloughs and 9% after reduced hours/income. One-quarter of those furloughed in April/May were out of the labor force at follow-up – evidence of a potential pathway from furloughs into retirement. Being employed at follow-up was most common after work-from-home in April/May (90%). Multi-level models revealed differences in mental health trajectories over six months according to baseline work disruptions, including persistently high anxiety following job loss and delayed upticks in anxiety and depressive symptoms when working from home. This research provides insights into longer-term economic and mental health ramifications of pandemic-related work disruptions among older workers.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".