Individuals’ positive gains from the COVID-19 pandemic: a qualitative study across 30 countries
Bibliographic record
Abstract
Background: Given the limited understanding of individuals' positive gains, this study aimed to identify these gains that could be leveraged by policymakers to enhance future health and societal resilience. Methods: We used a global qualitative approach to survey adults over 18 from 30 countries across six World Health Organization (WHO) regions, who detailed up to three personal positive gains from COVID-19 pandemic via an open-ended question. Inductive thematic analysis was employed to identify main themes, and quantitative methods were used for demographic and regional comparisons based on the percentage of responses for each theme. Results: From 35 911 valid responses provided by 13 853 participants, six main themes (one negative theme), 39 subthemes, and 673 codes were identified. Five positive gain themes emerged, ordered by response frequency: 1) improved health awareness and practices; 2) strengthened social bonds and trust; 3) multi-dimensional personal growth; 4) resilience and preparedness building; 5) accelerated digital transformation. The percentage of responses under these themes consistently appeared in the same order across various demographic groups and economic development levels. However, there were variations in the predominant theme across WHO regions and countries, with either Theme 1, Theme 2, or Theme 3 having the highest percentage of responses. Although our study primarily focused on positive gains, unexpectedly, 12% of responses (4304) revealed 'negative gains', leading to an unforeseen theme: 'Distrust and emerging vulnerabilities.' While this deviates from our main topic, we retained it as it provides valuable insights. Notably, these 'negative gains' had a higher percentage of responses in areas like Burundi (94.1%), Rwanda (31.8%), Canada (26.9%), and in the African Region (37.7%) and low-income (43.9%) countries, as well as among non-binary individuals, those with lower education, and those facing employment challenges. Conclusions: Globally, the identified diverse positive gains guide the domains in which health policies and practices can transform these transient benefits into enduring improvements for a healthier, more resilient society. However, variations in thematic responses across demographics, countries, and regions highlights need for tailored health strategies.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".