Exploring the Landscape of Social and Economic Factors in Critical Illness Survivorship: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: To explore the breadth of social, demographic, and economic (SDE) factors reported in critical illness survivorship research, with a focus on how they impact survivorship outcomes. DATA SOURCES: We obtained articles from Medline, Embase, PsycInfo, and CINAHL, as well as reference list reviews of included articles and relevant reviews captured by searches. STUDY SELECTION: SDE factors were defined as any nonmedical factor that can influence outcomes. We included primary studies published in English that explored SDE factors as an independent variable or as an outcome in post-ICU survivorship of adults. Two authors independently assessed each study for inclusion in duplicate, and conflicts were resolved by consensus. Our searches returned 7151 records, of which 83 were included for data extraction and final review. DATA EXTRACTION: We used a standardized data collection form to extract data, focusing on the characteristics of each study (i.e., year and country of publication), SDE factors explored, how the factors were measured, the impacts of SDE factors on post-ICU survivorship outcomes, and the impacts of ICU admission on SDE outcomes. DATA SYNTHESIS: We summarized the relationships between SDE factors and ICU survivorship in table format and performed a narrative synthesis. We identified 16 unique SDE factors explored in the current literature. We found that generally, higher education, income, and socioeconomic status were associated with better outcomes post-ICU; while non-White race, public insurance status, and social vulnerability were associated with poorer outcomes. CONCLUSIONS: Various SDE factors have been explored in the critical illness survivorship literature and many are associated with post-ICU outcomes with varying effect sizes. There remains a gap in understanding longitudinal outcomes, mechanisms of how SDE factors interact with outcomes, and of the complexity and interconnectedness of these factors, all of which will be instrumental in guiding interventions to improve post-ICU survivorship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".