Coping with Stressful Life Disruptions due to Long COVID: A Qualitative Study
Bibliographic record
Abstract
Study Materials for the project "Coping with Stressful Life Disruptions due to Long COVID: A Qualitative Study" funded by Canadian institutes of health research. The aim of this project was to explore the stressful disruptions experienced by PWLC and caregivers, their impact, and how PWLC and caregivers respond to and cope with them. A qualitative descriptive approach was employed to represent data in the participants’ own words as they made sense of their lived experiences, thereby making results more meaningful and relevant for justifying actionable change. In total, we interviewed 67 participants (n=52 PWLC and n=15 caregivers). Of note, two of the PWLC also reported being a caregiver to someone else that had long COVID. 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. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".