The integrated GOR-COVID-19 health monitor: quarterly reporting (“short-cycle monitoring”)
Bibliographic record
Abstract
Abstract Introduction/objective Given the capricious nature of COVID-19, infections and accompanying restrictive measures to reduce spread of the virus are known to strongly vary over time. The impact of the corona crisis on the mental and physical health of the general population may fluctuate accordingly. The quarterly monitoring research line is aimed to monitor the health impact of the corona crisis over time on a high frequency basis. Methods This is achieved through a multi-method approach, combining: a) survey data on perceived impact of corona measures and several health outcomes from two representative panels (∼ 5.000 adolescents/young adults aged 12 and 25 years; ∼ 5.000 adults aged 26+ years), and b) surveillance data of acute health problems from the Nivel Primary Care Database (∼1,6 million patients from 380 general practitioners). Data collection started in September 2021 and continues during the five-year monitoring program. Panel data is collected every quarter; Nivel surveillance data is collected weekly. Our contribution to the EUPHA conference will include data up and until September 2022 (i.e. five rounds of survey and surveillance data). Results Preliminary results from September 2021 through March 2022 indicate that adolescents/young adults reported worse mental and physical health outcomes compared to adults. Moreover, they experienced a more negative impact of corona measures. Accordingly, their mental health related problems, including suicide ideation, spiked between December 2021 and February 2022, a period characterized by highly restrictive COVID-19-measures. Discussion The quarterly monitoring research line showcase the relevance and feasibility of integrating multiple data sources in understanding the short- and long-term effect of the corona crisis. The results increase our understanding in the potential adverse effects of corona-related restrictive measures on population health, potentially aiding policy making and health promotion.
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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 | 0.045 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".