Implementation considerations for delivering inpatient COVID rehabilitation: A qualitative study
Bibliographic record
Abstract
RATIONALE: Patients recovering from significant COVID-19 infections benefit from rehabilitation; however, aspects of rehabilitative care can be difficult to implement amidst COVID infection control measures. AIMS AND OBJECTIVES: We used the Consolidated Framework for Implementation Research (CFIR) to evaluate the rapid implementation of a COVID zone in an in-patient rehabilitation hospital at the onset of the first wave of the pandemic. METHODS: Semistructured interviews were conducted with health care providers (n = 12) supporting the COVID zone, as well as with patients (n = 10) who were discharged from the COVID zone and their family caregivers (n = 5). The interviews explored the successes and challenges of working on the unit and the quality of care that was delivered to patients recovering from COVID. RESULTS: Rapid implementation of the COVID zone was supported by champions at the middle-management level but challenged by a number of factors, including: conflicting expert opinions on best infection control practices (outer setting), limited flow of information from senior leaders to frontline staff (inner setting), lack of rehabilitation equipment and understanding of how to provide high quality rehabilitative care in this context (intervention characteristics), willingness and self-efficacy of staff working in the COVID zone (individual characteristics) and lack of time to reflect on and assess effectiveness (process). CONCLUSIONS: While there was an apparent need for rapid implementation of a COVID rehabilitation zone, senior leadership, middle management and frontline staff faced several challenges. Future evaluations should focus on how to adapt COVID rehabilitation services during fluctuating pandemic restrictions, and to account for rehabilitative needs of people recovering from significant COVID infections.
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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 | 0.031 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".