Psychosocial Support Interventions for Adult Critically Ill Patients During the Acute Phase of Their ICU Stay: A Scoping Review
Bibliographic record
Abstract
Background: Addressing Intensive Care Unit (ICU) patients’ psychological well-being is crucial, yet psychosocial sup-port interventions that can facilitate effective coping, ultimately decreasing stress-related physiological, mental health and cognitive sequalae are not currently included in clinical practice guidelines and standards. Objective: To identify and synthesize research evidence on psychosocial support interventions in the ICU, including types of outcomes, measures of effectiveness and to explore research gaps and barriers to implementation. Method: The review was directed by a protocol based on current guidance for scoping reviews. Quality of studies were assessed using the National Institute for Health and Clinical Excellence. The review focused on articles containing evaluation of psychosocial interventions, through an experimental or quasi-experimental design, or pretest-posttest comparisons. Databases searched included Medline, CINAHL, PubMed, PsychInfo and the Cochrane Library. Results: Ten highly heterogeneous studies were identified, encompassing diverse intervention (e.g., relaxation, psychotherapy, spirituality), and patient populations. Notably, only three randomized controlled trials were found, with just one adequately powered to assess effectiveness. The predominance of quasi-experimental designs and small sample sizes resulted in low internal validity of the evidence. Despite these limitations, the evidence reviewed supports that various psychological interventions, including positive suggestions, relaxation techniques, psychotherapy and spiritual and/or religious support can alleviate psychological sequelae, such as depression, anxiety, and Post Traumatic Stress in ICU patients. Conclusion: This review highlights the positive impact of psychosocial interventions on alleviating psychological distress in ICU patients. How-ever, a critical gap exists in understanding their effects on other clinical and physiological outcomes, necessitating com-prehensive research.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".