Return-to-work for People Living with Long COVID: A Scoping Review of Interventions and Recommendations
Bibliographic record
Abstract
ABSTRACT Introduction Long COVID affects individuals’ labour market participation in many ways. While some cannot work at all, others may return to work (RTW) in a limited capacity. Determining what rehabilitation or related strategies are safe and effective for facilitating RTW is necessary. Objectives To synthesize evidence on RTW interventions for people living with Long COVID and to identify ‘promising’ interventions for enhancing work ability and RTW. Methods We followed Arksey & O’Malley’s methodology and the PRISMA extension for scoping reviews. Five electronic bibliographic databases and grey literature were searched. The included various study designs, such as randomized controlled trials (RCT), quasi-experimental designs, and observational studies. Two reviewers conducted screening and data extraction, with disagreements resolved through consensus. Intervention studies were categorized as promising (statistically significant RTW outcomes or ≥ 50% RTW), somewhat promising (20% to < 50% RTW), or not promising (non-statistically significant RTW outcomes or < 20% RTW). Results Eleven recommendations and eleven intervention studies were identified. Of the intervention studies, 6 were cohort studies, 3 quasi-experimental studies, 1 RCT and 1 case report. Promising interventions included multimodal and interdisciplinary work-focused rehabilitation (1 article), psychoeducation, pacing, and breathing strategies (2 articles), shifting focus from symptom monitoring to optimizing functional outcomes (1 article), and enhanced external CounterPulsation (EECP) inflatable pressure to improve blood flow (1 article). Conclusion Many uncertainties remain regarding which RTW interventions are effective or the optimal characteristics of these interventions.
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 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 | 0.040 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".