Priorities for Research, Education, Clinical Practice, and Policy From the Long COVID Physio International Forum
Bibliographic record
Abstract
Purpose: Long COVID Physio (LCP) is an international peer support, education and advocacy, patient-led association of physiotherapists living with long COVID and allies. In 2022, LCP hosted an international forum. One of the aims of the forum was to identify priorities in long COVID and rehabilitation. Methods: We conducted an international consultation on priorities for long COVID and rehabilitation with people living with long COVID, clinicians, researchers, and other key interest-holders (referred to collectively as “consultants”) who registered for and attended the LCP International Forum. We collected feedback from consultants using web-based questionnaires, the Zoom chat from the forum, and posts on an online platform during the forum. We analyzed data using group-based content analytical techniques. Priorities were organized into 4 categories: research, practice, education, and policy. Results: There were 794 respondents for the consultation representing 34 countries, including 47% (n = 376) living with long COVID. Seventeen priorities for long COVID overlapped and spanned research (epidemiology, socioeconomics, pathophysiology, characterizing disability, health equity, establishing diagnostic criteria, intervention studies), education (for people living with long COVID, employers, policy makers, and health care professional students), clinical practice (safety, person-centered approaches), and policy (accessibility of care, supports for people living with long COVID and caregivers, public health messaging). Priorities were focused on long COVID and rehabilitation, but some extended beyond the scope of rehabilitation (eg, pharmacological interventions). Conclusions: These priorities can help to guide research, clinical practice, education, and policy, to advance health outcomes for people living with long COVID.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".