The use of health care during the SARS-CoV-2 pandemic: repeated cross-sectional survey of the adult Swiss general population
Bibliographic record
Abstract
BACKGROUND: The distribution of health care resources during a pandemic is challenging. The aim of the study was to describe the use of health care in a representative sample of the Swiss population during the SARS-CoV-2 pandemic in 2020, and to compare it to data from a survey conducted in 2018. METHODS: We conducted an observational, population-based, nationwide, repeated cross-sectional survey of the adult Swiss general population in 2018 and in March and April 2020 during the first wave of the SARS-CoV-2 pandemic. Recruitment and data acquisition was conducted by the Link Institute in Lucerne in representative samples of Swiss citizens in 2020 and in 2018. Variables of interest were estimates of health problems, health seeking behaviour, medication and health care use in the population. RESULTS: In total, we included data of 1980 individuals (in 2018 N = 958 and in 2020 N = 1022). Across both rounds of data collection the median age was 46 years (range = 18-79 years) and 50% were women. Per 1000 adults, half had at least one symptom and a quarter sought medical advice across both surveys. The most frequently consulted health providers in 2020 were general practitioners (GP) (180/1000), specialist physicians (41/1000), pharmacies (38/1000), the internet (26/1000) and accident and emergency units (25/1000). Compared to 2018, we noted a significant increase in the use of health providers during the pandemic, which was independent of demographic variables for the following health care providers: use of internet (OR = 9.8), pharmacy (OR = 2.64), accident and emergency units (OR = 2.54), and a significant decrease in the number of people who consulted specialist physicians (OR = 0.46). Overall, 76/1000 contacted their GP in relation to COVID-19. CONCLUSIONS: Compared to 2018, GPs remained the most important source of medical advice for the population during the first wave of the COVID-19 pandemic in Switzerland. While the self-appraisal of health problems and of the need for medical advice remained constant, individuals seemed to change their provider choice during the pandemic, with an increased utilisation of accident and emergency units and pharmacies, which represent easily accessible and low-threshold medical services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".