Factors to consider when designing post-hospital interventions to support critical illness recovery: Systematic review and qualitative evidence synthesis
Bibliographic record
Abstract
Background: Survivors of intensive care unit (ICU) admission experience significant deficits in health-related quality of life due to long-term physical, psychological, and cognitive sequelae of critical illness, which may persist for many years. There has been a proliferation of post-hospital interventions in recent years which aim to support ICU-survivors, however there is currently limited evidence to inform optimal approach. We therefore aimed to synthesise factors which impacted the implementation of these interventions from the perspective of healthcare providers, patients, and their carers, and to compare different intervention designs. Methods: We conducted a systematic review and synthesis of qualitative evidence using four databases (MEDLINE, EMBASE, CINAHL and Web of Science) which were searched from inception to May 2024. The extraction and synthesis of factors which impacted intervention implementation was informed by the domains of the Consolidated Framework for Implementation Research (CFIR) and Template for Intervention Description and Replication (TIDieR) checklist. Results: Thirty-seven studies were included, reporting on a range of interventions including follow-up clinics and rehabilitation programmes. We identified some overarching principles and specific intervention component and design factors which may support in the design of future strategies to improve outcomes for ICU survivors. For each intervention characteristic, various patient, staff, and setting factors were found to impact implementation. Considering how the intervention will rely on and integrate with existing outpatient and community resources is likely to be important. Conclusion: This review provides a framework to future research examining the optimal approach to supporting ICU survivor recovery following hospital discharge.
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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 | 0.189 | 0.290 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".