Coping with stressful life disruptions due to long COVID: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Long COVID impacts people's physical health and cognition which immensely affects their psychosocial well-being. A larger study was conducted that explored the psychosocial impacts of Long COVID on individuals and caregivers. This paper focuses on the impact of these stressful disruptions on one's health and psychosocial well-being, and how individuals cope with them. METHODS: Utilizing a qualitative descriptive approach, we conducted interviews with 67 participants (52 people with Long COVID (mean age: 50.4) and 15 caregivers (mean age: 50.0)). People with Long COVID and caregivers were recruited from healthcare institutions through care team referrals, patient partners, and health organizations and via social media platforms. A thematic codebook developed through inter-coder agreement processes was used to analyze the data. FINDINGS: Three key themes were identified: (1) Disruptions in people with Long COVID and caregivers' lives are characterized by a deviation from their perceived 'normalcy', (2) Disruptions lead to substantial stress, loss and grief (independence, agency, meaning, and purpose), and (3) People with Long COVID and caregivers cope with stressful disruptions by adapting their daily activities. CONCLUSION: Our findings make the case for supportive rehabilitation strategies that address the psychosocial repercussions of Long COVID to help mitigate feelings of loss and grief, thereby increasing individuals' overall quality of life and well-being.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".