Using spatial video geonarratives to improve nursing care for people who use drugs and experience homelessness: A methodology for nurses
Bibliographic record
Abstract
BACKGROUND: People who are insecurely housed and use drugs are disproportionately affected by drug poisonings. Nurses are uniquely positioned to utilize harm reduction strategies to address the needs of the whole person. Needle debris encompasses drug paraphernalia discarded in public spaces. Studying needle debris provides a strategic opportunity to identify where drugs are being used and target public health strategies accordingly. AIM: Our aim in this article is to illustrate how spatial video geonarratives (SVG) combined GPS technology interviews, and videos of locations with needle debris, can elicit valuable data for nursing research. METHODS: Using SVG required knowledge of how to collect data wearing cameras and practice sessions were necessary. A Miufly camera worn at waist height on a belt provided the stability to walk while interviewing stakeholders. We wore the cameras and conducted go-along interviews with outreach workers, while filming the built environment. Upon completion of data collection, both the interview and GPS information were analysed using Wordmapper software. CONCLUSIONS: This methodology resulted in data presented uniquely in both a visual map and narrative. These data were richer than if a single modality had been used. These data highlighted specific contextual factors that were related to the location of needle debris, which created opportunities for nursing interventions to support people experiencing vulnerability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".