Scoping review of rehabilitation care models for post COVID-19 condition
Bibliographic record
Abstract
Objective: To systematically map the current evidence about the characteristics of health systems, providers and patients to design rehabilitation care for post coronavirus disease 2019 (COVID-19) condition. Methods: We conducted a scoping review by searching the databases: MEDLINE®, Embase®, Web of Science, Cochrane COVID-19 Registry and Cochrane Central Register of Controlled Trials, from inception to 22 April 2022. The search strategy included terms related to (i) post COVID-19 condition and other currently known terminologies; (ii) care models and pathways; and (iii) rehabilitation. We applied no language or study design restrictions. Two pairs of researchers independently screened title, abstracts and full-text articles and extracted data. We charted the evidence according to five topics: (i) care model components and functions; (ii) safe delivery of rehabilitation; (iii) referral principles; (iv) service delivery settings; and (v) health-care professionals. Findings: We screened 13 753 titles and abstracts, read 154 full-text articles, and included 37 articles. The current evidence is conceptual and expert based. Care model components included multidisciplinary teams, continuity or coordination of care, people-centred care and shared decision-making between clinicians and patients. Care model functions included standardized symptoms assessment, telehealth and virtual care and follow-up system. Rehabilitation services were integrated at all levels of a health system from primary care to tertiary hospital-based care. Health-care workers delivering services within a multidisciplinary team included mostly physiotherapists, occupational therapists and psychologists. Conclusion: Key policy messages include implementing a multilevel and multiprofessional model; leveraging country health systems' strengths and learning from other conditions; financing rehabilitation research providing standardized outcomes; and guidance to increase patient safety.
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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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".