Addressing drug-related harms: the roles of community health nurses
Bibliographic record
Abstract
Background. In Manitoba, drug-related harms such as drug poisonings, fatalities and emergency presentations have increased significantly, especially over the course of the COVID-19 pandemic. Other harms include blood-borne infections, stigma and discrimination, and legal harms. Community health nurses are well positioned, in theory, to address these harms in their practice. However, little is known about their perspectives and specific roles and practices in addressing drug-related harms. Purpose. The purpose of this study was to explore community health nurses’ perspectives and their roles in addressing drug-related harms. Their views and experiences with drugs were explored along with their thoughts of the “drug problem” and the current responses to the problem, including government and healthcare responses. Design. Sally Thorne’s qualitative research method of interpretive description was the methodology chosen for this study. Sample and setting. Participants included licensed nurses (i.e., RNs, RPNs, and LPNs) working in the community, including public health nurses, primary care nurses and nurses working in addictions services recruited from various community organizations in Winnipeg. Methods. Data was collected using semi-structured interviews and field notes. Interviews were conducted virtually using Zoom videoconferencing technology. Thematic analysis guided by the Framework Method was used to analyze data. Findings. The findings showed that community health nurses predominantly view the “drug problem” as a social issue rather than an individual one. They stress the importance of addressing root causes of drug-related harms within the context of social determinants of health. Despite this perspective, community health nurses continue to provide mainly individual level care with minimal community or systems level interventions. This misalignment is associated with moral distress. A number of systemic and organizational barriers that limit their ability to perform system level roles were identified. Conclusions. Community health nurses are well-positioned to address drug-related harms in their practice. However, the majority of their work is comprised of individual level care which fails to address underlying inequities experienced by their clients. Efforts are needed to support community health nurses to perform upstream or system-level roles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".