Psychological distress and digital health service use during COVID-19: A national Australian cross-sectional survey
Bibliographic record
Abstract
Background Previous research suggests that the COVID-19 pandemic caused significant disruption to the lives and mental health of Australians. In response, health services adapted rapidly to digital modes of treatment, prevention and care. Although a large amount of research emerged in the first year of the pandemic, the longer-term mental health impacts, contributing factors, and population-level utilization of digital health services are unknown. Methods A population-based online survey of 5,100 Australians adults was conducted in October 2021. Psychological distress was assessed with the Kessler 6-item Psychological Distress Scale. Additional survey questions included use and satisfaction with digital health services. Where available, data were compared with our previous survey conducted in 2018, permitting an examination of pre- and post-pandemic digital health service utilization. Results In 2021, almost a quarter (n = 1203, 23.6%) of respondents reported serious levels of psychological distress; participants with pre-existing health related conditions, of younger age, lower educational attainment, those who lost their job or were paid fewer hours, or living in states with lockdown policies in place were at highest risk of serious psychological distress. Almost half of all respondents (n = 2177, 42.7%) reported using digital health technologies in 2021, in contrast to just 10.0% in 2018. In 2021, respondents with serious psychological distress were significantly more likely to consult with a healthcare professional via telephone/videoconferencing (P < 0.001), access healthcare via a telephone advice line (P < 0.001), or via an email or webchat advice service (P < 0.001) than those with no serious psychological distress. Those with and without psychological distress were highly satisfied with the care they received via digital health technologies in 2021. Conclusion Rates of serious psychological distress during the second year of the pandemic remained high, providing further evidence for the serious impact of COVID-19 on the mental health of the general population. Those with psychological distress accessed digital mental health services and were satisfied with the care they received. The results highlight the continued need for mental health support and digital health services, particularly for people living with chronic conditions, younger adults and people most impacted by the COVID-19 pandemic, both in the short term and beyond.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".