Investigating the Impacts of Urban Risk Environments: A Focused Ethnographic Study of People Who Use Drugs in Central Business Districts
Bibliographic record
Abstract
High rates of public drug use in urban areas are a significant public health and social issue. Drug use and open-air drug markets in and around central business districts (CBDs) have attracted particular attention and concern in the peri-pandemic period. People who use drugs (PWUD) who spend most of their time in these areas face significant health risks, social stigma, legal issues, and constrained access to support services leading to exacerbated morbidity and mortality risks. In response to limited research in this area, this study investigates how features of central business district risk environments impact the daily lives of PWUD. My thesis aims to address this knowledge gap by prioritizing the lived experiences and perspectives of PWUD with the goals of investigating how features of central business district risk environments impact lived daily experiences of risk and well-being for PWUD; service accessibility for PWUD in these settings; and can be altered to better support the health and safety of PWUD and other at-risk community members in these environments. Together, findings from this thesis will shed light on the impacts, risks, and opportunities urban central business districts may pose for PWUD, and crucially, will provide recommendations on comprehensive responses to help improve the health and safety of PWUD and the broader community. I begin this proposal with an introductory chapter where I provide background on the epidemiology of drug use in Canada, risk factors and intersections, and the unique relationship between urban public drug use and health. I next discuss the theoretical background and then highlight key knowledge gaps and research aims addressed by my thesis. I conclude this chapter by describing the methodology guiding my thesis. Chapters two and three outline the logistics and parameters of each subsequent study, including relevant literature, respective rationales, research questions, and study methods. Chapter four synthesizes and discusses the significance of these findings in context of the overarching thesis, potential contributions to literature, and implications for research, policy, programs, and practice innovations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".