Gender differences in the occurrence of farm related injuries.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: To use national surveillance data in Canada to describe gender differences in the pattern of farm fatalities and severe injuries (those requiring hospitalisation). METHODS: Data from the Canadian Agricultural Injury Surveillance Program (CAISP) included farm work related fatalities from 1990 to 1996 for all Canadian provinces and abstracted information from hospital discharge records from eight provinces for the five fiscal years of 1990 to 1994. Gender differences in fatalities and injuries were examined by comparison of proportions and stratified by sex, injury class (machinery, non-machinery), and age group. RESULTS: Over the six year period of 1990 to 1996 there were approximately 11 times as many agriculture related fatalities for males compared to females (655 and 61, respectively). The most common machinery mechanisms of fatal injuries were roll-over (32%) for males and run-over (45%) for females. Agricultural machinery injuries requiring hospitalisation showed similar patterns, with proportionally more males over age 60 injured. The male:female ratio for non-machinery hospitalisations averaged 3:1. A greater percentage of males were struck by or caught against an object, whereas for females, animal related injuries predominated. CONCLUSIONS: Gender is an important factor to consider in the interpretation of fatal and non-fatal farm injuries. A greater number of males were injured, regardless of how the occurrence of injury was categorised, particularly when farm machinery was involved. As women increasingly participate in all aspects of agricultural production, there is a need to collect, interpret, and disseminate information on agricultural injury that is relevant for both sexes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it