Injuries related to consumer products in Canada–a systematic literature review
Bibliographic record
Abstract
OBJECTIVES: To conduct a systematic literature review of injury related to certain consumer products. METHODS: Forty-six empirical research reports along with 32 surveillance reports from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) were examined to determine the approximate number of injuries associated with a given product per year and any trends in frequency. Percentages of injuries that: (1) resulted in hospitalization, (2) appeared to result from the product itself and (3) were associated with risky or inappropriate use and/or non-use of a helmet were also extracted from the reports. RESULTS: Outdoor play and sports equipment appear to be associated with the greatest numbers of injuries. A relatively high proportion of injuries appear to result from inappropriate or risky use of a product and/or inadequate safety precautions. CONCLUSION: This review identified the following areas of concern regarding consumer products and injuries: lack of helmet use by people using in-line skates, sleds, snowboards, downhill skis and personal-powered watercraft; operation of all-terrain vehicles (ATVs) and snowmobiles by alcohol-impaired people; operation of snowmobiles at excessive speeds; poor design of playground equipment; and unsafe storage and use of matches.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.025 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".