Models of Peer Support to Remediate Post-Intensive Care Syndrome: A Report Developed by the Society of Critical Care Medicine Thrive International Peer Support Collaborative*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Patients and caregivers can experience a range of physical, psychologic, and cognitive problems following critical care discharge. The use of peer support has been proposed as an innovative support mechanism. DESIGN: We sought to identify technical, safety, and procedural aspects of existing operational models of peer support, among the Society of Critical Care Medicine Thrive Peer Support Collaborative. We also sought to categorize key distinctions between these models and elucidate barriers and facilitators to implementation. SUBJECTS AND SETTING: Seventeen Thrive sites from the United States, United Kingdom, and Australia were represented by a range of healthcare professionals. MEASUREMENTS AND MAIN RESULTS: Via an iterative process of in-person and email/conference calls, members of the Collaborative defined the key areas on which peer support models could be defined and compared, collected detailed self-reports from all sites, reviewed the information, and identified clusters of models. Barriers and challenges to implementation of peer support models were also documented. Within the Thrive Collaborative, six general models of peer support were identified: community based, psychologist-led outpatient, models-based within ICU follow-up clinics, online, groups based within ICU, and peer mentor models. The most common barriers to implementation were recruitment to groups, personnel input and training, sustainability and funding, risk management, and measuring success. CONCLUSIONS: A number of different models of peer support are currently being developed to help patients and families recover and grow in the postcritical care setting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it