Integrating peer support across the continuum of trauma care: Trauma survivor, caregiver and healthcare provider perspectives and recommendations
Bibliographic record
Abstract
BACKGROUND: Recovery from a traumatic injury is a complex process that precipitates difficulties and isolation for survivors. Peers can provide valuable psychosocial support rooted in lived experience. The savings associated with peer support largely outweigh the costs. Despite this, research has yet to explore the ideal components of a cross-continuum peer support program or the factors that might impact its delivery. OBJECTIVES: Understand the barriers/facilitators to integrating peer support across the continuum of care; and (2) Identify recommendations for the design and delivery of a cross-continuum peer support program. METHODS: Qualitative descriptive approach. Interviews were conducted with trauma survivors (n = 16), caregivers (n = 4), and healthcare providers (HCPs) (n = 16). We employed an inductive thematic analysis to identify barriers and facilitators. We also conducted a deductive analysis using a framework for peer support interventions in physical medicine and rehabilitation to identify what should be included in a cross-continuum peer support program. RESULTS: Barriers and facilitators included: (1) individual-level issues, (2) the physical and social environment, (3) clinical practice considerations, (4) finance and resourcing, and (5) organization/system issues. Peer support programming should be introduced early in recovery and continue into community living. Peer support programming should be offered flexibly (virtually or in-person) and provide: (1) education, (2) empowerment; and (3) social support. Participants agreed that a person with lived experience should be trained and centrally involved. CONCLUSIONS: When designing peer support programming, we must consider who would benefit from support, what support should consist of, and ideal timing and mode of support delivery.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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".