Experiences of pacing to reduce symptoms among adults living with Long COVID in Canada, Ireland, the United Kingdom and the United States
Bibliographic record
Abstract
Long COVID is a multisystem condition that negatively impacts daily function. Pacing is a self-management strategy to mitigate symptoms. Our aim was to describe experiences of pacing from the perspectives of adults living with Long COVID. We conducted a community-engaged qualitative descriptive study involving one-on-one online interviews with adults living with Long COVID from Canada, Ireland, United Kingdom, and United States to explore experiences of disability. We asked participants about strategies they used to deal with health challenges living with Long COVID. Interviews were audio recorded and transcribed verbatim. We analyzed data using group-based content analytical techniques. Among the 40 participants living with Long COVID, the majority were women ( n = 25; 63%), white ( n = 29;73%) and heterosexual ( n = 30;75%). The median age of participants was 39 years (25th, 75th percentile: 32, 49). Most participants ( n = 37;93%) used pacing to mitigate or prevent symptoms. Participants described experiences of pacing across five main areas: (1) using pacing as a living strategy (pacing to mitigate multidimensional health challenges; applying pacing to many types of activities; process of pacing experienced as a moving target; pacing experienced as a helpful strategy, but not a cure for Long COVID); (2) learning how to pace (acquiring knowledge about pacing; developing strategies and skills to support pacing); (3) encountering challenges with pacing (learning how to pace; experiencing inequitable access to pacing; experiencing stigma and judgement; undergoing psychological and emotional adjustment from beliefs of ‘fighting’ or ‘pushing through’ to balancing rest with activity; making sacrifices; and encountering unexpected obstacles); (4) experiencing consequences of not pacing; and (5) conceptualising and describing pacing using analogies or metaphors. Pacing is a challenging and complex strategy used to mitigate symptoms of Long COVID. Healthcare providers should work collaboratively with patients to further refine and implement this strategy, when appropriate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".