Lived experiences of COVID-19 disease: a qualitative meta-synthesis
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, various aspects of human life were changed around the world. The present study aimed to provide a systematic review of the available evidence on lived experiences of the COVID-19 pandemic. Methods: This is a systematic review of the meta-synthesis type. Evidence from studies from 2019 to 2021 was used. Keywords of lived experiences, experiences, people, nation, patients, community, COVID-19, corona, and corona disease were searched in PubMed, Science Direct, Web of Science, and Cochrane databases. The Newcastle-Ottawa scale was used to evaluate the quality of articles. A qualitative meta-synthesis was performed by a researcher based on a three-step meta-synthesis method described by Thomas and colleagues. MAXQDA 10 was used for data analysis. The present study is based on the guidelines for Enhancing transparency in reporting the meta-synthesis of qualitative research (ENTREQ). The reliability of this study had a Kappa coefficient of 0.660 with a consistency rate of 98.766%. Results: Finally, the data from 11 articles were analyzed. The main and sub-themes obtained in this study included negative aspects (chaos, hustle associated, dualities, bad body, value decay, seclusion, psychological challenges) as well as positive aspects (opportunities arising from the individual, family, and social stability). Conclusion: Problems of life during COVID-19 should be considered as part of the COVID-19 pandemic care program. Individual assessments should normally be considered in a public health crisis. It is recommended to conduct serious, in-depth, and practical research in this field.
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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 | 0.075 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".