Disempowered Warriors: Insights on Psychological Responses of ICU Patients Through a Meta-Ethnography
Bibliographic record
Abstract
Objectives: to systematically examine and synthesize qualitative evidence on adult patients’ psychological distress during an intensive care unit stay to inform development of interventions tailored to their needs. Method: We conducted systematic literature searches in CINAHL, MEDLINE, EMBASE, PsycINFO, Scopus, Dissertations and Theses Global, and Google Scholar databases using predefined eligibility criteria. We synthesized primary qualitative research evidence using Noblit and Hare’s meta-ethnographic approach. Reporting was based on the eMERGe framework. The quality of included articles was assessed by the Critical Appraisal Skills Program tool. Findings: We identified 31 primary studies from 19 countries. The studies were of moderate to high quality. Data analysis revealed five themes: “disempowerment”, “altered self-identity” “fighting”, “torment”, and “hostile environment”. One overarching theme, “the disempowered warrior”, captured the perpetual tension between the need to fight for their lives and the need to succumb to the care process. Our synthesis discloses that critically ill patients perceive themselves to be in a battle for their lives; while at the same time they may feel helpless and disempowered. Conclusions: Our review revealed the tension between the need to fight for one’s life and the sense of powerlessness in the intensive care unit environment. Although participants recognize the important role of healthcare workers, they desired more involvement, collaboration, control, empathy, and empowerment in the care process. These findings can inform approaches to empowering critically ill patients and managing their psychological responses. Care standards must include distress assessment and management that maximize patients’ empowerment and emotional safety with the care process.
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 | 0.069 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".