The ICU Bridge Program: volunteers bridging medicine and people together
Bibliographic record
Abstract
BACKGROUND: The intensive care unit (ICU) is an emotionally taxing environment. Patients and family members are at an increased risk of long-term physical and psychological consequences of critical illness, known collectively as post-intensive care syndrome (PICS). These environmental strains can lead to a high incidence of staff turnover and burnout. AIM: The ICU Bridge Program (ICUBP) is a student-led organization that attempts to mitigate these stressors on patients, family, and staff, by assigning university volunteers to ICUs across Montreal. SETTING: ICU. PARTICIPANTS: ICU volunteers, staff, patients, and families. PROGRAM DESCRIPTION: The ICUBP volunteers support staff by orienting patients and families, while using effective communication strategies to provide comfort and promote a calm environment. The presence of volunteer visitors is helpful to patients who do not have the support of family members and/or friends. The program provides students with profound learning experiences by allowing them to shadow multidisciplinary teams, gaining a privileged and varied exposure to an acute medical environment, while developing their communications skills. PROGRAM EVALUATION: The program reassesses its methods and impact via internal student-designed surveys distributed on a yearly basis to staff and volunteers. DISCUSSION: Research is warranted to assess the impact of the program on ICU patients, visitors, staff, and volunteers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".