Impacts of the COVID-19 lockdown on self-reported mood and self-rated health of community-dwelling adults with chronic illness
Bibliographic record
Abstract
AIM: To determine whether self-reported mood or self-rated health were affected in community-dwelling adults with chronic illness following COVID-19 lockdown. METHODS: This was a repeated cross-sectional study using secondary data. We included New Zealanders aged 40+ who underwent International Residential Instrument (interRAI) assessments in the year prior to COVID-19 lockdown (25 March 2019-24 March 2020) or in the year following COVID-19 lockdown (25 March 2020-24 March 2021). Pairwise comparisons were made between each pre-lockdown quarter and its respective post-lockdown quarter to account for seasonality patterns. Data from 45,553 (pre-lockdown) and 45,349 (post-lockdown) assessments were analysed. Outcomes (self-reported mood, self-rated health) were stratified by socio-demographic variables. RESULTS: Self-reported mood improved in the first quarter post-lockdown among those aged 80+, as well as among women, people of European ethnicity, those living alone and those living in more deprived areas. Self-rated health improved in these same groups, as well as among those aged 65-79, and among men. No differences in self-reported mood or self-rated health were found in the second, third, or fourth quarters post-lockdown. CONCLUSIONS: Self-reported mood and self-rated health of community-dwelling adults with chronic illness were not negatively affected following COVID-19 lockdown, and temporarily improved among some sub-groups. However, the longer-term impacts of the COVID-19 pandemic need to be closely monitored.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".