Conceptualising the Episodic Nature of Disability among Adults Living with Long COVID: A Qualitative Study
Bibliographic record
Abstract
ABSTRACT Objectives To describe episodic nature of disability among adults living with Long COVID. Methods We conducted a community-engaged qualitative descriptive study involving online semi-structured interviews and participant visual illustrations. We recruited participants via collaborator community organizations in Canada, Ireland, United Kingdom, and United States. Participants Adults who self-identified as living with Long COVID. We purposively recruited for diversity in age, gender, race/ethnicity, sexual orientation, and duration since initial COVID-19 infection. Main Outcome Measure(s) We used a semi-structured interview guide to explore experiences of disability living with Long COVID, specifically health-related challenges and how they were experienced over time. We asked participants to draw their health trajectory and conducted a group-based content analysis. Results Among the 40 participants, the median age was 39 years (interquartile range: 32, 49); majority were women (63%), white (73%), heterosexual (75%), and living with Long COVID for ≥1 year (83%). Participants described their disability experiences as episodic in nature, characterized by fluctuations in presence and severity of health-related challenges (disability) that may occur both within a day and over the long-term living with Long COVID. They described living with ‘ups and downs’, ‘flare-ups’, and ‘peaks’ followed by ‘crashes’, ‘troughs’, and ‘valleys’, likened to a ‘yo-yo’ ‘rolling hills’, and ‘rollercoaster ride’ with ‘relapsing/remitting’, ‘waxing/waning’, ‘fluctuations’ in health. Drawn illustrations demonstrated variety of trajectories across health dimensions, some more episodic than others. Uncertainty intersected with the episodic nature of disability, characterized as unpredictability of episodes, their length, severity and triggers, and process of long-term trajectory, which had implications on broader health. Conclusions Among this sample of adults living with Long COVID, experiences of disability were described as episodic, characterized by fluctuating health challenges, which may be unpredictable in nature. Results help to better understand experiences of disability among adults living with Long COVID to inform healthcare and rehabilitation. KEY MESSAGES What is already known on this topic: Globally, a growing number of individuals are living with persistent and prolonged signs and symptoms following infection consistent with COVID-19, referred to as Long COVID, Post COVID-19 Condition (PCC) or Post-acute sequelae of SARS-CoV2 (PASC). Individuals living with Long COVID are experiencing a range of symptoms and impairments that impact their ability to carry out day to day activities or engage in social and community life roles. What this study adds: Disability living with Long COVID was described as episodic, characterized by fluctuations in presence and severity of health related challenges, which may be unpredictable in nature, occurring both within the day, and over the long-term of months and years living with Long COVID. How this study might affect research, practice or policy: Results will help researchers, healthcare providers, policymakers, employers, and community members to better understand experiences of disability among adults living with Long COVID, to inform future disability measurement, health and rehabilitation care and service delivery, programs and policies for insurance, return to work, and workplace accommodations.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| 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".