Scanning and Mapping Culturally Responsive Harm Reduction Services
Bibliographic record
Abstract
As research indicates higher mortality rates among racialized people who use drugs, SOPEN is interested in a comprehensive scan of existing models of culturally responsive HR services across Canada to inform service development and knowledge mobilization. By mapping these organizations and understanding their approaches, SOPEN seeks to facilitate inter-organizational learning and collaboration within the HR sector. This resource aims to benefit multiple stakeholders, including SOPEN's volunteers and leadership, HR service providers, advocacy organizations, and community groups seeking to enhance their cultural responsiveness. The McMaster Research Shop has agreed to support this project by answering the following primary and secondary research questions: 1. What organizations across Canada are currently implementing or piloting culturally responsive HR programs that serve ethnoculturally diverse communities, including racialized populations, newcomers, immigrants, and 2SLGBTQ+ individuals? 2. What are the key features, strategies, and best practices characterizing culturally responsive HR models within these organizations? The report summarizes the research team's 1) approach to the scan and 2) the findings, including an overview of ethnoculturally diverse services in 9 major Canadian municipalities and 3 case studies highlighting exemplary services.
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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".