“I feel like my body is broken”: Exploring the experiences of people living with long COVID
Bibliographic record
Abstract
Abstract Background Long COVID, an illness affecting a subset of individuals after COVID-19, is distressing and poorly understood. Exploring the experiences of people with long COVID could help inform current conceptualizations of the illness, guide supportive care strategies, and validate patients’ perspectives on the condition. Thus, the objective of this study was to better understand and explore individuals’ experiences with long COVID and commonly reported symptoms, using qualitative data collected from open-ended survey responses. Methods Data were collected from adults living with long COVID following a confirmed or suspected SARS-CoV-2 infection who participated in a larger observational, online survey. Within the larger survey, participants had the option of answering seven open-ended items. Data from the open-ended items were analyzed following guidelines for reflective thematic analysis. Results From the 213 who were included in the online survey, 169 participants who primarily self-identified as women (88.2%), aged 40-49 (33.1%), and who had been experiencing long COVID symptoms for ≥ 6 months (58.6%) responded to the open-ended questions. Four overlapping and interconnected themes were identified: (1) My long COVID symptoms are numerous, hard to describe, and debilitating , (2) All aspects of my day-to-day functioning have been impacted , (3) I can no longer be physically active , and (4) I keep asking for help, but no one is listening, and very little is working . Conclusion Findings highlight the complex nature of long COVID and show the ways in which individuals affected by the illness are negatively impacted. Participants recounted struggling and altering their daily activities while managing relapsing-remitting symptoms, an uncertain prognosis, lost pre-COVID identities, and a healthcare system (that does not always offer guidance nor take them seriously). More support and recognition for the condition are needed to help this cohort navigate the process of adapting to long COVID.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".