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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".