Impact of the first wave of COVID‐19 on the health and psychosocial well‐being of Māori, Pacific Peoples and New Zealand Europeans living in aged residential care
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of New Zealand's (NZ) first wave of COVID-19, which included a nationwide lockdown, on the health and psychosocial well-being of Māori, Pacific Peoples and NZ Europeans in aged residential care (ARC). METHODS: interRAI assessments of Māori, Pacific Peoples and NZ Europeans (aged 60 years and older) completed between 21/3/2020 and 8/6/2020 were compared with assessments of the same ethnicities during the same period in the previous year (21/3/2019 to 8/6/2019). Physical, cognitive, psychosocial and service utilisation indicators were included in the bivariate analyses. RESULTS: A total of 538 Māori, 276 Pacific Peoples and 11,322 NZ Europeans had an interRAI assessment during the first wave of COVID-19, while there were 549 Māori, 248 Pacific Peoples and 12,367 NZ Europeans in the comparative period. Fewer Māori reported feeling lonely (7.8% vs. 4.5%, p = 0.021), but more NZ Europeans reported severe depressive symptoms (6.9% vs. 6.3%, p = 0.028) during COVID-19. Lower rates of hospitalisation were observed in Māori (7.4% vs. 10.9%, p = 0.046) and NZ Europeans (8.1% vs. 9.4%, p < 0.001) during COVID-19. CONCLUSIONS: We found a lower rate of loneliness in Māori but a higher rate of depression in NZ European ARC populations during the first wave of COVID-19. Further research, including qualitative studies with ARC staff, residents and families, and different ethnic communities, is needed to explain these ethnic group differences. Longer-term effects from the COVID-19 pandemic on ARC populations should also be investigated.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| Research integrity | 0.001 | 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".