Occupation Disrupted: Impacts, Challenges, and Coping Strategies For Farmers with Disabilities
Bibliographic record
Abstract
Agriculture has been recognized as one of the most dangerous industries in Canada and the United States. Yet, the impacts of injuries on Canadian farmers and farm families, from the perspective of those affected, have not been investigated. This article highlights findings from a study initiated by the Canadian Farmers with Disabilities Registry (CFDR). An occupational framework, in this case the Model of Human Occupation, is used to examine the impact of disability on the work, leisure, family, and social occupations that comprise farm life. Quantitative questions on a survey drew responses from 47 of 111 (42%) CFDR members. Qualitative questions on the survey were supplemented with in‐depth telephone interviews with eight farmers of various ages, length of time with disability, and farming experiences in all regions of Canada. The results of the study profile participants’ characteristics as well as the causes, forms and sources of occupational disruption, and their responses to it The occupational disruption experienced by farmers with disabilities is a story of unnecessary tragedy. There are major policy implications related to community, manufacturing, government, insurance, banking, and other financial supports for farmers with disabilities who risk losing the opportunity to choose farming as their occupation and lifestyle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".