What Can We Learn from 21 Years of School Poisoningsin New Zealand?
Bibliographic record
Abstract
Background: Childhood poisoning is a significant international health concern. Very little is known about trends in exposures within schools and preschools. The objectives of this study were to investigate the data recorded by the New Zealand National Poisons Centre (NPC) on these types of exposures over a 21 year period (1989 to 2009) and to determine trends and propose strategies to reduce the exposures. Methods: Call information regarding human poison exposure at preschools and schools from Jan1st 1989 to Dec 31st 2009 were extracted from the dataset held by the NPC. The number of calls received by the NPC relating to the exposures was plotted against year as totals and then categorized according to gender. The number of calls related to each substance type for each year, and the number of calls related to each age group for each year were quantified. Results: There were 3632 calls over this period. In every year studied, there were more calls relating to males than females. Household items were responsible for 31% of exposures, followed by plants (20%), industrial items (14%) and therapeutic agents (14%). Almost one quarter of all exposures occurred in the 13 year old age group. Further investigation of this group, showed that the causes of exposures included “splash” incidents (27%), “pengestion” (pen breaking in mouth and releasing contents) (16%), “exploratory” (5%) and “prank” (4%). Conclusion: Identification of these areas allows recommendations to be made including feedback to teachers about exposure risks, storage and access of science, cleaning and art supplies.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".