Life After ICU: The patient experience with ICU and after hospital discharge
Bibliographic record
Abstract
ICU can be a life-altering experience for patients and their families. ICU survivors may face a multitude of complications after their hospital stay that have been collectively termed Post Intensive Care Syndrome (PICS). In the current global environment of the COVID-19 pandemic, ICUs across the world have seen a drastic increase in ICU patients. It has been recognized within the ICU community that a better understanding of the post-ICU population is needed as there is a lack of understanding about what the ICU survivor experiences after hospital discharge and what resources may support them to manage best or avoid PICS. To examine the experience of former ICU patients, the qualitative method of Interpretive Description was utilized. Patients who participated in the ICU recovery clinic in Alberta, Canada, were invited to participate in a 45-minute interview about their experience with ICU and their life after hospital discharge. Three former ICU patients were interviewed virtually to share their experiences. The results of this study found themes in data analysis that include communication challenges, living with uncertainty, and post-ICU fallout. These themes do not occur in isolation but are interconnected and can impact the other themes, which ultimately can impact ICU survivors’ recovery. Participants expressed frustration with various communication challenges they experienced both during and after ICU, and the impact of visitation restrictions on their experience. Participants also expressed emotion about the uncertainty they face after an ICU admission, including uncertainty about what happened in ICU and uncertainty with their present and future. Finally, participants discussed their experience of living with the fallout of an ICU admission and the benefit of attending the ICU recovery clinic. The results of this study highlight the need for broader awareness and education for health professionals on the potential complications faced by ICU survivors after discharge. This study also shows support for specialized follow-up services, like the ICU recovery clinic. Specialized follow-up allowed each of the participants to be viewed holistically, beyond their medical diagnosis or physical impairment, and for the first time each had their experience normalized.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".