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Participants’ Perspectives on Joining a Virtual Rehabilitation Program for Long COVID: A Qualitative Study

2025· article· en· W4410271539 on OpenAlexaff
Kathryn Agarwal, Catherine M. Tansey, Marla Beauchamp, Bryan Ross, Jean Bourbeau, Anthony Rizk, Maria Sedeno, L Barreto, Rebecca Zucco, Eileen Crowley, Tania Janaudis‐Ferreira

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsMcMaster UniversityMcGill University Health CentreMcGill University
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)RehabilitationQualitative researchBetacoronavirusCoronavirus InfectionsPhysical therapyPhysical medicine and rehabilitationVirologyPathology

Abstract

fetched live from OpenAlex

Abstract Rationale: Our team has completed a randomized controlled trial of virtual rehabilitation in long COVID. Given the diverse range of symptoms reported in individuals with long COVID and with an ongoing debate on the role of exercise for this condition, questions persist regarding the willingness to engage in this type of intervention. We aimed to explore the perspectives of individuals with long COVID who underwent an 8-week virtual rehabilitation program on their confidence in performing exercises at home as part of virtual rehabilitation program, their reaction when offered to participate in the program and reasons for participating in the trial. Methods: The study randomized 132 individuals with long COVID into either an intervention group (receiving 8-week virtual rehabilitation program along with education and usual care) or a control group (receiving usual care only). Thirteen semi-structured interviews were conducted with participants of the intervention group at the end of their 8-week program. Each interview was conducted individually via Zoom by an experienced female research associate and was digitally recorded. The data were analyzed using Deductive Thematic Analysis. Results: We identified three preliminary themes: 1) Confidence, guidance, and concerns: Most participants expressed confidence in performing exercises at home. Some participants, while feeling prepared to start exercising, were waiting to receive proper guidance by a professional. A few participants had concerns regarding their ability to perform exercises, post-exertional malaise symptoms, or time commitment. 2) “I'm the lucky one": When finding out they had been assigned to the exercise group, all participants expressed positive feelings such as being “happy,” “enthusiastic,” and “hopeful”. Some described as being the “lucky one” and “being looked after.” 3) Potential benefits and contribution to science: Participants’ reasons for joining the trial were multifaceted. They were seeking answers to better understand their new health condition, hoping to improve their symptoms and physical condition, and access essential health services through the trial. Additionally, they wanted to contribute scientific knowledge to help those experiencing similar challenges. Conclusion: Our findings reveal that participants felt confident and motivated to engage in the virtual rehabilitation program, reassured by professional support and driven by personal health goals and a desire to contribute to research on long COVID.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0100.008
Scholarly communication0.0040.004
Open science0.0030.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.036
GPT teacher head0.457
Teacher spread0.422 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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