Self-managing symptoms of Long COVID: an education and strategies research protocol
Bibliographic record
Abstract
Post-acute sequelae of SARS-COV-2 (PASC) is growing in prevalence, and involves symptoms originating from the central neurological, cardiovascular, respiratory, gastrointestinal, autonomic nervous, or immune systems. There are non-specific symptoms such as fatigue, headaches, and brain fog, which cannot be ascribed to a single system. PASC places a notable strain on our healthcare system, which is already laden with a large number of acute-COVID-19 patients. Furthermore, it impedes social, academic and vocational functioning, and impacts family life, relationships, and work/financial life. The treatment for PASC needs to target this non-specific etiology and wide-ranging sequelae. In conditions similar to PASC, such as “chemo brain,” and prolonged symptoms of concussion, the non-specific symptoms have shown to be effectively managed through education and strategies for self-management and Mindfulness interventions. However, such interventions have yet to be empirically evaluated in PASC to our knowledge. In response to this gap, we have developed a virtual education intervention synthesized by psychiatrists and clinical psychologists for the current study. We will undertake a two-phase randomized controlled trial to determine the feasibility (Phase 1; N = 90) and efficacy (Phase 2; sample sized based on phase 1 results) of the novel 8 week Education and Self-Management Strategies group compared to a mindfulness skills program, both delivered virtually. Main outcomes include confidence/ability to self-manage symptoms, quality of life, and healthcare utilization. This study stands to mitigate the deleterious intrusiveness of symptoms on everyday life in patients with PASC, and may also help to reduce the impact of PASC on the healthcare system. Clinical trial registration: https://classic.clinicaltrials.gov/ct2/show/NCT05268523 ; identifier NCT05268523.
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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 | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.065 | 0.014 |
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".