Deaths Associated With Community Donation Bins: A Ten-Year Retrospective Review Describing Five Cases in British Columbia and Ontario
Bibliographic record
Abstract
INTRODUCTION: Community donation bins have become more common in the urban setting over the past several years. Many nonprofit organizations use these sturdy metal enclosures for unobserved collection of various donated items such as clothing, books, and household items. Although the donated items are often of low individual value, donation bins may become a target of individuals in low socioeconomic situations seeking desired items for personal use or resale, or for personal shelter within the bin. METHODS: To identify donation bin-associated deaths, we reviewed cases taken under the jurisdiction of the coroner for investigation in the provinces of British Columbia and Ontario, Canada, over the years 2009 to 2019. RESULTS: We present the circumstances and postmortem findings of five deaths that occurred in British Columbia and Ontario (Canada) between 2009 and 2019, wherein the decedents were each believed to have been reaching into donation bins and became caught within the door mechanism and died as a consequence of compression asphyxia involving the chest and/or neck. DISCUSSION: Donation bins have the potential for harm when individuals attempt to access the bin contents through the entry portal. We advocate for greater attention and changes in the placement location and/or design of these potentially dangerous devices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".