Using Internet-Based Psychological Measurement to Capture the Deteriorating Community Mental Health Profile During COVID-19: Observational Study
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease (COVID-19) is expected to have widespread and pervasive implications for mental health in terms of deteriorating outcomes and increased health service use, leading to calls for empirical research on mental health during the pandemic. Internet-based psychological measurement can play an important role in collecting imperative data, assisting to guide evidence-based decision making in practice and policy, and subsequently facilitating immediate reporting of measurement results to participants. OBJECTIVE: The aim of this study is to use an internet-based mental health measurement platform to compare the mental health profile of community members during COVID-19 with community members assessed before the pandemic. METHODS: This study uses an internet-based self-assessment tool to collect data on psychological distress, mental well-being, and resilience in community cohorts during (n=673) and prior to the pandemic (two cohorts, n=1264 and n=340). RESULTS: Our findings demonstrate significantly worse outcomes on all mental health measures for participants measured during COVID-19 compared to those measured before (P<.001 for all outcomes, effect sizes ranging between Cohen d=0.32 to Cohen d=0.81. Participants who demonstrated problematic scores for at least one of the mental health outcomes increased from 58% (n=197/340) before COVID-19 to 79% (n=532/673) during COVID-19, leading to only 21% (n=141) of measured participants displaying good mental health during the pandemic. CONCLUSIONS: The results clearly demonstrate deterioration in mental health outcomes during COVID-19. Although further research is needed, our findings support the serious mental health implications of the pandemic and highlight the utility of internet-based data collection tools in providing evidence to innovate and strengthen practice and policy during and after the pandemic.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".