Long COVID Optimal Health Program (LC-OHP) to Enhance Psychological and Physical Health: Protocol for a Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Long COVID is a collection of symptoms that develop during or following a confirmed or suspected case of COVID-19, which continue for more than 12 weeks. Despite the negative impact of long COVID on people's lives and functioning, there is no validated treatment or even rehabilitation guidance. What has been recommended thus far is the adoption of holistic management approaches. The Optimal Health Program (OHP) is a brief 5-session, plus booster, psychosocial program designed to support mental and physical well-being that has been used effectively for a range of chronic conditions. OBJECTIVE: This study examines the feasibility and acceptability of employing an especially customized version of OHP (long COVID OHP [LC-OHP]) to improve psychological and physical health of people with long COVID. METHODS: This is a feasibility randomized controlled trial that will be running from November 2021 to February 2023. Eligible participants aged 18 years or older who are experiencing symptoms of long COVID will be identified through their secondary practitioners with recruitment to be undertaken by the research team. A total of 60 participants will be randomized into a control (usual care) or an intervention (LC-OHP) group. Outcomes will be feasibility and acceptability of the program (primary); and efficacy of the LC-OHP in improving anxiety, depression, fatigue, self-efficacy, and quality of life (secondary). Up to 20 participants will be interviewed at the end of the trial to explore their experience with the program. Quantitative data will be analyzed using SPSS, and differences between groups will be compared using inferential tests where appropriate. Qualitative data will be transcribed and thematically analyzed to identify common emerging themes. RESULTS: This is an ongoing study, which began in November 2021. CONCLUSIONS: Long COVID has a significant impact on an individual's mental and physical functioning. The LC-OHP has a potential to provide people living with long COVID with additional support and to improve self-efficacy. The findings of this study would identify the feasibility of delivering this program to this population and will provide an indication for the program's effectiveness. TRIAL REGISTRATION: ISRCTN Registry ISRCTN38746119; https://www.isrctn.com/ISRCTN38746119. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36673.
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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 | 0.048 | 0.034 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.099 | 0.016 |
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".