Reaching out to Patients with Long COVID to Better Understand Their Life Experiences and How to Support Their Recovery: A Patient-Oriented Knowledge Sharing Session
Bibliographic record
Abstract
This article reports on participants' experiences with long COVID-19 (LC) (symptoms, impact, healthcare use, and perceived needs) and satisfaction with a patient-oriented knowledge-sharing session organized by a multidisciplinary team of healthcare professionals, researchers, and a patient partner. Twenty-six participants completed a pre-session survey. On average, they were 21 months post-COVID-19 infection (SD 10.9); 81% of them were female, and 84% were 40+ years old. The main symptoms reported included fatigue (96%), cognitive problems (92%), and general pain or discomfort (40%). More than half of the participants reported that LC has had a significant impact on their health-related quality of life. Eighty-one percent of the participants reported seeking medical help for their LC symptoms and found the services provided by physical therapists, primary care providers, and acupuncturists to be helpful in managing their condition. Participants would like to have access to healthcare providers and clinics specializing in LC. They liked the session and found the information presented useful. This information helps to better understand the experiences of people living with LC and how to support their recovery.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".