Mixed methods evaluation of the impact of the COVID-19 ICU remote-learning rehabilitation course for frontline health professionals during the COVID-19 pandemic in the UK
Bibliographic record
Abstract
Background Optimising outcomes for critically ill patients with COVID-19 patients requires early interdisciplinary rehabilitation. As admission numbers soared through the pandemic, the redeployed workforce needed rapid, effective training to deliver these rehabilitation interventions. Methods The COVID-19 ICU Remote-Learning Rehab Course (CIRLC-rehab) is a one-day interdisciplinary course developed after the success of CIRLC-acute. The aim of CIRLC-rehab was to rapidly train healthcare professionals to deliver physical, nutritional and psychological rehabilitation strategies in the ICU/acute setting. The course used blended learning with interactive tutorials delivered by shielding critical care professionals. CIRLC-rehab was evaluated through a mixed-methods approach, including questionnaires, and follow-up semi-structured interviews to evaluate perceived impact on clinical practice. Quantitative data are reported as n (%) and means (SD). Inductive descriptive thematic analysis with methodological triangulation was used to analyse the qualitative data from the questionnaires and interviews. Results 805 candidates completed CIRLC-rehab. 627 (78.8%) completed the post-course questionnaire. 95% ( n = 596) found CIRLC-rehab extremely or very useful and 96.0% ( n = 602) said they were very likely to recommend the course to colleagues. Overall confidence rose from 2.78/5 to 4.14/5. The course promoted holistic and humanised care, facilitated informal networks, promoted interdisciplinary working and equipped the candidates with practical rehabilitation strategies that they implemented into clinical practice. Conclusion This pragmatic solution to educating redeployed staff during a pandemic increased candidates’ confidence in the rehabilitation of critically ill patients. There was also evidence of modifications to clinical care utilising learning from the course that subjectively facilitated holistic and humanised rehabilitation, combined with the importance of recognising the humanity, of those working in ICU settings themselves. Whilst these data are self-reported, we believe that this work demonstrates the real-term benefits of remote, scalable and rapid educational delivery.
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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 | 0.053 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".