ReportNeedles.ca: A Real-time Needle Collection Tool to Foster Community Health Partnerships
Bibliographic record
Abstract
Technology-mediated interactions between the public, health care agencies, and researchers can facilitate community health partnerships. The use of such novel technologies can lead to innovations in public health research to address disparities and access issues. This article presents the web-based, real-time needle collection tool ReportNeedles.ca and describes its use in an ongoing community health partnership to deploy and evaluate pop-up interventions for blood-borne infection prevention and substance use harm reduction. Since April 2021, 34,350 needles have been collected from 466 public reports on the ReportNeedles.ca app in the city of Regina, Saskatchewan (population: approximately 215,000). This non-walkable city with pronounced needle prevalence may be representative of medium-sized cities in Canada, the United States, and elsewhere, where brick-and-mortar health care is predominantly accessible only to people of relative stability. This article discusses the tool's development, implementation, and evaluation plan alongside its potential for blood-borne infection prevention, harm reduction, and community-based participatory research.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.012 |
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".