Consensus Recommendations for Maintaining Neurorehabilitation Quality During Healthcare Crises: A Stakeholder-Informed Mixed Methods Study
Bibliographic record
Abstract
The COVID-19 pandemic significantly disrupted neurorehabilitation practices in Inpatient Rehabilitation Facilities (IRFs), providing an opportunity to examine crisis response strategies. This mixed-methods study examined the pandemic's impact on quality of care through 3 complementary phases: (1) multivariable medical record analysis across 6 neurorehabilitation facilities in a pre-pandemic reference group (n = 134), a non-COVID group (n = 138), a COVID-positive group infected prior to admission ((COV+prior, n = 87) and a group infected during rehabilitation (COV+rehab, n = 36); (2) qualitative analysis of stakeholder consultations with patients (n = 26), staff members (n=55), and managers (n = 7); and (3) integration of quantitative and qualitative results to develop stakeholder and consensus-based recommendations (n = 12 participants). While non-COVID patients (n = 138) maintained pre-pandemic outcome levels, COVID-positive patients showed reduced functional independence at discharge, with distinct trajectories based on infection timing, even after controlling for status at admission. Patients infected during rehabilitation (n = 36) experienced longer stays and higher readmission rates compared to those infected pre-admission (n = 87). Qualitative analysis identified psychosocial, discharge planning, and resource management issues affecting both COVID-positive and non-COVID patients, emphasizing the impact of social isolation. Integration of findings led to 5 consensus-based recommendations: adaptation of COVID unit environments, family involvement, socialization promotion, safe care trajectory planning and flexible local management. These findings highlight the need for balanced approaches between infection control and rehabilitation quality during healthcare crises, guided by local leadership.
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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 | 0.395 | 0.359 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".