Peer support workers as a bridge: a qualitative study exploring the role of peer support workers in the care of people who use drugs during and after hospitalization
Bibliographic record
Abstract
BACKGROUND: To describe the key qualities and unique roles of peer support workers in the care of people who inject drugs during and after hospitalization. METHODS: We conducted a qualitative study. Key stakeholders were recruited including: people who use drugs who had been hospitalized, healthcare team members, peer support workers, and employers of peer support workers. Data were collected from 2019 to 2020 using semi-structured interviews that were audio-recorded, transcribed, and analyzed thematically. RESULTS: Fourteen participants were interviewed: 6 people who use drugs who had been hospitalized, 5 healthcare team members, 2 peer support workers, and 1 employer of peer support workers. At the core of the data was the notion of peer workers acting as a bridge. We found four themes that related to functions of this bridge: overcoming system barriers, advocacy, navigating transitions within the healthcare system, and restoring trust between HCPs and PWUD. We found two themes for building a strong bridge and making the role of a peer support worker function effectively (training and mentorship, and establishing boundaries). We found three themes involving characteristics of an effective peer worker (intrinsic qualities, contributions of shared experiences, and personal stability). CONCLUSION: Peer support workers are highly valued by both people who use drugs and members of the healthcare team. Peer support workers act as a bridge between patients and healthcare providers and are critical in establishing trust, easing transitions in care, and providing unique supports to people who use drugs during and after hospitalization.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".