The diversity of opinion among general practitioners regarding the threat and measures against COVID-19 – Cross-sectional survey
Bibliographic record
Abstract
Background After the ‘first wave’ in spring 2020, opinions regarding the threat and measures against COVID-19 seemed to vary among German general practitioners (GPs).Objectives To systematically investigate opinions and to identify subgroups of GPs sharing similar views.Methods A questionnaire was sent to all 210 practices accredited for undergraduate teaching of family medicine at the Medical Faculty of the Technical University of Munich. Questions addressed personal opinions regarding risks, dilemmas, restrictions and their relaxation associated with COVID-19, and personal fears, symptoms of depression and anxiety. Patterns of strong opinions (‘archetypes’) were identified using archetypal analysis, a statistical method seeking extremal points in the multidimensional data.Results One hundred and sixty-one GPs sent back a questionnaire (response rate 77%); 143 (68%) with complete data for all 38 relevant variables could be included in the analysis. We identified four archetypes with subgroups of GPs tending in the direction of these archetypes: a small group of ‘Sceptics’ (n = 12/8%) considering threats of COVID-19 as overrated and measures taken as exaggerated; ‘Hardliners’ (n = 34/24%) considering threats high and supporting strong measures; ‘Balancers’ (n = 77/54%) who also rated the threats high but were more critical about potentially impairing the quality of life of elderly people and children; and ‘Anxious’ GPs (n = 20/14%) tending to report more fear, depressive and anxiety symptoms.Conclusion Among the participants in this survey, opinions regarding the threat and the measures taken against COVID-19 during the ‘first wave’ in Germany in spring 2020 varied greatly.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".