The lockdown and its consequences—Perspectives and needs of people at increased risk of severe illness from COVID-19
Bibliographic record
Abstract
BACKGROUND: There is a lack of knowledge on how people at increased risk of severe illness from Coronavirus disease 2019 (COVID-19) experienced the infection control measures. This study aimed to explore their perspectives and needs during the coronavirus outbreak. METHODS: A qualitative longitudinal interview study was conducted in Austria during lockdown due to COVID-19 containment and afterwards. People older than 65 years of age and/or affected by a chronic medical condition participated in individual telephone interviews at two time points. Thematic analysis was used to analyze the data and saturation was defined as no new emerging concepts in at least 10 subsequent interviews. RESULTS: Thematic saturation was reached when 33 individuals (75.8% female, mean age ± standard deviation [SD] 73.7±10.9 years) were included. A total of 44 lower level concepts were extracted and summarized into 6 higher level concepts. They included (i) a general positive attitude toward COVID-19 measures, (ii) challenges of being isolated from the community, (iii) deterioration of health status, (iv) difficulties with measures due to their health condition, (v) lack of physical contact and (vi) lack of information versus overload. Participants suggested environmental adaptations for strengthening resilience in people at increased risk of severe illness from COVID-19. CONCLUSION: Strategies and interventions are needed to support people at risk under pandemic conditions. Their perceptions and needs should be addressed to reduce the potential deterioration of health conditions and ensure well-being even during prolonged periods of crisis.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".