Peer support experiences and needs across the continuum of trauma care: A qualitative study of traumatic injury survivor, caregiver, and provider perspectives
Bibliographic record
Abstract
BACKGROUND: Traumatic injuries significantly impact individuals' physical and mental health and are a leading cause of disability worldwide. Trauma recovery is complex and entails patients interacting with multiple places of care before returning to the community. Despite trauma recovery being optimized when patients' psychosocial needs are addressed early on and throughout recovery, care remains overwhelmingly focused on physical and functional improvement. Peer support is a cost-effective way of providing emotionally and experientially-driven psychosocial support that complements usual patient care. Thus, we aimed to explore the experiences of trauma survivors, family caregivers, and healthcare providers (HCPs) with engaging in and facilitating peer support and to identify their priorities for a future peer support program. METHODS: Qualitative descriptive approach. Trauma survivors, caregivers and HCPs were recruited from three major trauma centres in Ontario. We conducted one-one-one interviews with participants which were recorded and transcribed. Data was thematically analyzed by multiple analysts to reduce bias and enhance data reliability. RESULTS: We interviewed n=16 trauma survivors, n=4 caregivers, and n=16 HCPs. We identified four themes: (1) "It's a major change": Navigating life after injury is challenging and characterized by uncertainty; "I just needed somebody just to talk to:" Peer support helps trauma survivors feel like they're not alone; (3) "You can learn off each other": Peer support is multi-faceted and facilitates recovery in ways that other supports cannot; and (4) "If other people say negative things…that makes things worse": Tensions exist between the benefits of peer support and the risk of unintended negative consequences. Overall, to meet trauma survivors' socialization needs and enhance the efficacy of interventions, it is recommended that peer support to be offered via a range of modalities. CONCLUSIONS: Our study demonstrates that peer support is valued across stakeholders and has the potential to positively impact the psychosocial health of trauma survivors throughout recovery. Future development of a cross-continuum peer support program will consider how to connect peers early on after injury and sustain these relationships into community recovery.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".