Rapid review and commentary on the clinical implications of the population mental health consequences of the COVID-19 pandemic in Australia
Bibliographic record
Abstract
OBJECTIVE: To provide a rapid clinical review and commentary for psychiatrists on the population mental health consequences of the COVID-19 pandemic in Australia, including evidence-based findings and interventions. CONCLUSIONS: Whilst there was evidence of collective psychological resilience during the first 2 years of the COVID-19 pandemic, younger women, carers for those with COVID-19, and those with more household chores, childcare needs and higher economic strain, were at more risk. Interventions should therefore target people with these socio-demographic risk factors, as well as severe COVID-19 survivors, their relatives and frontline workers. However, the rapid spread of the Omicron SARS-CoV-2 variant has the potential for greater impacts on population mental health. Innovations in telehealth and online therapy should be incorporated into standard care. Ongoing research is needed to assess who remains most vulnerable to negative mental health impacts of the current pandemic, and especially the longer term outcomes of mental ill health. Further research should also investigate evidence-based approaches to resilience and well-being. Prospective risk/benefit analyses of infection control measures, economic effects and mental health consequences are needed.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".