Additional file 1 of Time trends in social contacts of individuals according to comorbidity and vaccination status, before and during the COVID-19 pandemic
Bibliographic record
Abstract
Additional file 1: Table S1. Distribution of comorbidity status among confirmed cases in Quebec adults. Table S2. STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies. Table S3. Classifications of active physical comorbidities. Table S4. Sample size calculations. Table S5. Key socio-demographic characteristics of participants, by period. Table S6. Time trends in the mean number of social contacts of individuals with and without active physical comorbidities. Table S7. Time trends in the mean total number of social contacts of individuals with and without active physical comorbidities at risk of COVID-19 complications. Table S8. Vaccination coverage with one dose of individuals with and without active physical comorbidities in the third wave. Table S9. Recommended Quebec priority groups for vaccination against COVID-19. Table S10. Mean total number of social contacts in the third wave of individuals with and without active physical comorbidities according to vaccination status with one dose. Figure S1. Distribution of participants according to classifications of comorbidities. Figure S2. Time trends in vaccination coverage with one dose of individuals with and without active physical comorbidities in the third wave. Example of questions S1. Questions on health conditions. Example of questions S2. Example of the social contact diary.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.716 | 0.061 |
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".