Exploring stakeholder perspectives on nurse-led telehealth services for people who use substances: A study protocol
Bibliographic record
Abstract
Background: In Canada, the Covid-19 pandemic and the overdose crisis have made it more difficult for people who use substances to access medical care. This difficulty is due to the lack of appropriate harm reduction services. In addition, the unwelcoming attitudes and the stigma they face from medical personnel represent a significant barrier. The accelerated and significant shift of healthcare services from in-person to virtual delivery has opened new avenues to provide timely and appropriate healthcare services to people who use substances. Low-threshold nurse-led initiatives are particularly promising in that regard due to the broad scope of nursing practice in Canada. Objective: To better understand stakeholders’ perspectives on the needs of people who use substances, as well as the feasibility and acceptability including conditions, barriers, and facilitators, of the implementation of a nurse-led telehealth service for this population. Methods: We will conduct interviews with relevant stakeholders from diverse backgrounds and origins, including people who use substances, healthcare professionals such as nurses, intervention workers on an addiction support line, community and social workers, and peer workers. Verbatim transcripts of the interviews will be analyzed using NVivo. Results: Thematic analysis will identify common and group-specific themes related to experiences with substance use, perceptions of telehealth, and service needs. Similarities and differences across participant groups will be explored to inform a tailored and feasible nurse-led telehealth model for PWUS. Conclusions: This fundamental knowledge will be useful to guide the development of a low-threshold nurse-led telehealth service for people who use substances in the province of Quebec, Canada.
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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 | 0.084 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".