‘ <i>You’re alive, but are you living?</i> ’ Exploring long COVID (LC)’s impact on social and leisure well-being for individuals and caregivers
Bibliographic record
Abstract
Long COVID (LC) affects physical health and cognition, limiting participation in social and leisure activities. As a novel disabling condition following a COVID-19 infection, informal caregivers of those with LC have taken on expanded roles, including educating themselves on this diagnosis. Gathering insights from people living with LC (PWLC) and their caregivers is crucial for understanding its impact on well-being and identifying targeted rehabilitation practices across the LC care pathway. Utilizing a qualitative descriptive approach, we conducted interviews with 67 participants (52 people with LC and 15 caregivers). Results: Composite narratives were created to introduce three key themes: (1) The demands of managing physical and cognitive symptoms of LC limit the ability of PWLC and caregivers to engage in social and leisure activities; (2) The loss of meaningful social and leisure activities deepens a diminished sense of identity for PWLC and caregivers; and (3) The absence of shared social and leisure engagement intensifies feelings of disconnection and loneliness for PWLC and caregivers. The findings support a need for more social- and leisure-targeted interventions for LC rehabilitation to help to optimize efforts for coping with the psychosocial impacts of living with, or caring for someone with, LC.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".