Long-Term Post–COVID-19 Health and Psychosocial Effects and Coping Resources Among Survivors of Severe and Critical COVID-19 in Central and Eastern Europe: Protocol for an International Qualitative Study
Bibliographic record
Abstract
BACKGROUND: There is a strong need to determine pandemic and postpandemic challenges and effects at the individual, family, community, and societal levels. Post-COVID-19 health and psychosocial effects have long-lasting impacts on the physical and mental health and quality of life of a large proportion of survivors, especially survivors of severe and critical COVID-19, extending beyond the end of the pandemic. While research has mostly focused on the negative short- and long-term effects of COVID-19, few studies have examined the positive effects of the pandemic, such as posttraumatic growth. It is essential to study both negative and positive long-term post-COVID-19 effects and to acknowledge the role of the resources available to the individual to cope with stress and trauma. This knowledge is especially needed in understudied regions hit hard by the pandemic, such as the region of Central and Eastern Europe. A qualitative approach could provide unique insights into the subjective perspectives of survivors on their experiences with severe COVID-19 disease and its lingering impact on their lives. OBJECTIVE: The aim of the study is to qualitatively explore the experiences of adult survivors of severe or critical COVID-19 throughout the acute and postacute period in 5 Central and Eastern European countries (Bulgaria, Slovakia, Croatia, Romania, and Poland); gain insight into negative (post-COVID-19 condition and quality of life) and positive (posttraumatic growth) long-term post-COVID effects; and understand the role of survivors' personal, social, and other coping resources and local sociocultural context and epidemic-related situations. METHODS: This is a qualitative thematic analysis study with an experiential reflexive perspective and inductive orientation. The analytical approach involves 2-stage data analysis: national analyses in stage 1 and international analysis in stage 2. Data are collected from adult survivors of severe and critical COVID-19 through in-depth semistructured interviews conducted in the period after hospital discharge. RESULTS: As of the publication of this paper, data collection is complete. The total international sample includes 151 survivors of severe and critical COVID-19: Bulgaria (n=33, 21.8%), Slovakia (n=30, 19.9%), Croatia (n=30, 19.9%), Romania (n=30, 19.9%), and Poland (n=28, 18.5%). National-level qualitative thematic analysis is currently underway, and several papers based on national results have been published. Cross-national analysis has started in 2024. The results will be submitted for publication in the third and fourth quarters of 2024. CONCLUSIONS: This research emphasizes the importance of a deeper understanding of the ongoing health and psychosocial challenges survivors face and what helps them cope with these challenges and, in some cases, thrive. It has implications for informing holistic care and improving the health and psychosocial outcomes of survivors of COVID-19 and will be crucial for evaluating the overall impact and multifaceted implications of the pandemic and for informing future pandemic preparedness. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57596.
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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 | 0.046 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.005 |
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".