“Whatever journey you want to take, I’ll support you through”: a mixed methods evaluation of a peer worker program in the hospital emergency department
Bibliographic record
Abstract
Abstract Background People who are unhoused, use substances (drugs and/or alcohol), and who have mental health conditions experience barriers to care access and are frequently confronted with discrimination and stigma in health care settings. The role of Peer Workers in addressing these gaps in a hospital-based context is not well characterized. The aim of this evaluation was to 1) outline the role of Peer Workers in the care of a marginalized populations in the emergency department; 2) characterize the impact of Peer Workers on patient care, and 3) to describe how being employed as a Peer Worker impacts the Peer. Methods Through a concurrent mixed methods evaluation, we explore the role of Peer Workers in the care of marginalized populations in the emergency department at two urban hospitals in Toronto, Ontario Canada. We describe the demographic characteristics of patients (n = 555) and the type of supports provided to patients collected through a survey between February and June 2022. Semi-structured, in-depth interviews were completed with Peer Workers (n = 7). Interviews were thematically analyzed using a deductive approach, complemented by an inductive approach to allow new themes to emerge from the data. Results Support provided to patients primarily consisted of friendly conversations (91.4%), discharge planning (59.6%), tactics to help the patient navigate their emotions/mental wellbeing (57.8%) and sharing their lived experience (50.1%). In over one third (38.9%) of all patient interactions, Peer Workers shared new information about the patient with the health care team (e.g., obtaining patient identification). Five major themes emerged from our interviews with Peer Workers which include: (1) Establishing empathy and building trust between the patient and their care team through self-disclosure; (2) Facilitating a person-centered approach to patient care through trauma-informed listening and accessible language; (3) Support for patient preferences on harm reduction; (4) Peer worker role facilitating self-acceptance and self-defined recovery; and (5) Importance of supports and resources to help Peer Workers navigate the emotional intensity of the emergency department. Conclusions The findings add to the literature on Peer Worker programs and how such interventions are designed to best meet the needs of marginalized populations.
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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 | 0.056 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".