ICU Bridge Program: Working with staff towards no family members feeling like "the elephant in the room"
Bibliographic record
Abstract
The intensive care unit (ICU) provides specialized care to critically ill patients. Given the traumatic nature of critical illness and its treatments, up to 75% of family members of ICU decedents and survivors experience long-term psychological consequences, termed post-intensive care syndrome family (PICS-F). Anxiety, PTSD, and depression are common manifestations that significantly impact families’ quality of life and the recovery of those dependent on their caregiving. Although PICS-F can be mitigated by engagement with ICU staff, critical care workers are at risk of burnout and requesting closer liaisons with families is unfeasible. Bridging visitors and the ICU health care team would ensure that family members never feel like “the elephant in the room”.The ICU Bridge Program (ICUBP) is a unique volunteering and shadowing initiative designed and run by university students. Bridge Program volunteers are assigned to hospital ICUs in Montreal to be the first point of contact for visitors. This program addresses PICS-F by humanizing the ICU experience through compassionate human contact, continuous support, and an open line of communication. The diverse applicants are carefully selected and trained to maximize soft skills, such as emotional intelligence and active listening, which ensures that families feel welcome and understood in this tense environment. Furthermore, the ICUBP’s self-sufficient structure off-loads administrative responsibilities from resource-constrained hospitals and makes its implementation feasible and cost-efficient. By continuously monitoring its effect on patients, families, and staff, the ICUBP aims to improve and expand its contribution to whole-person care in the ICU.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".