Meeting people where they are at : how nurses, using the framework of harm reduction, make sense of nursing practice with people who use drugs
Bibliographic record
Abstract
Nurses who work with people who use drugs in the Downtown Eastside of Vancouver (DTES), British Columbia are on the forefront in advancing a harm reduction framework in very controversial, cutting-edge practice environments. The purpose of this study was to explore how these nurses, using the framework of harm reduction, make sense of their nursing practice. It is hoped that the results of this study may advance adopting a harm reduction framework in nursing practice, education and policy development and serve as the foundation for further nursing research. This study utilized a qualitative interpretive descriptive methodology to gather data from eight nurses who work with people who use drugs in harm reduction practice environments. The nurses were divided into two focus groups and data was collected through a semi-structured focus group interview. Following initial data analysis, each focus group was reconvened and a second semi-structured group interview was held to clarify and to further discuss the emerging themes. The data analysis proceeded simultaneously with the interviews utilizing a process of constant comparative analysis. I completed the thematic analysis as I moved between the transcripts and identified commonalties and variations within the emerging themes. Ultimately, I described one overarching theme, which encapsulated the range of experiences described by the nurses. The theme that I identified was: meeting people where they are at. The importance to the nurses of both the therapeutic nurse-client relationship and a commitment to praxis were apparent. In conclusion, the value the nurses placed on "meeting people where they are at" was integral in gaining an understanding of how they make sense of their nursing work.
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.046 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".