Exploring the value and acceptability of a patient navigator program for people who inject drugs and are hospitalized for bacterial infections: patients’, community organization and healthcare workers’ perspectives
Bibliographic record
Abstract
BACKGROUND: Hospitalizations for bacterial infections are often difficult for people who inject drugs (PWID) and healthcare workers, in part due to biases and stigma associated with substance use, patients' competing needs, such as pain and withdrawal management, and strict antibiotic treatment protocols. In recent years, peer navigators have been introduced as a strategy to reduce stigma and bridge the gap between patients and healthcare workers, but little is known about their involvement in hospitalization settings. The aim of this study was to assess the value of adding a peer navigator program and to evaluate the elements that key stakeholders identified as essential for the program to be successful. METHODS: This was a qualitative study using focus groups. The interview guide was collaboratively developed by ethicists, physicians, and a person with lived experience and validated with a PWID and a community worker. Three two-hour focus groups were conducted in February 2022 with PWID, community organizations and healthcare workers. Descriptive and interpretive thematic analyses were carried out. RESULTS: Nineteen people (5 PWID, 6 community organization workers, 8 healthcare workers) participated in the focus groups. The final coding strategy involved 4 main themes: challenges in current care, positive aspects of current care, aspirations for quality care, the contribution of peer navigators as a solution to current challenges and the realization of aspirations. Improvements in the quality of care should focus on an approach centered on patients' values and aspirations; improving the current hospital environment, particularly in terms of training and communication; and encouraging collaborative partnerships with all parties involved. The integration of peer navigators seems to be a promising strategy for improving communication and trust and, consequently, to facilitate shared decision-making and adapted care. CONCLUSIONS: Our study showed that any innovative model should be centered on patients' needs and values and therefore co-constructed with them and other parties involved, notably the community organizations offering services to these patients. The inclusion of well-trained and well-supported peer navigators has the potential to improve care and work toward achieving aspirations of quality care.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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 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".