Community Support Systems for Farmers Who Live With Disability
Bibliographic record
Abstract
To return to farming following the onset of a disabling injury or health condition is predicated on a supportive environment for the farm owner-operator. The purpose of this study was to examine barriers and facilitators of returning to work after an injury or acquired disability, and to identify community supports (formal and informal) needed and available to farmers. This was a qualitative study using a participatory action research approach that involved a research team, an advisory group of seven stakeholder participants and semistructured interviews with farmers, their spouses, and service providers in the study communities. A total of 11 farmers and 17 service providers took part in a focus group or were interviewed individually with or without the spouse present. Focus group discussions and interviews were transcribed and analyzed using constant comparison method and team consensus of findings. Themes of barriers and facilitators for return to farming were identified as health care services, financial and economic issues, and "hands-on" assistance. Themes were organized into an ecological conceptual diagram depicting barriers and facilitators in the microsystem of the farm family, the mesosystem of the local farming community and regional health jurisdictions, and the macrosystem of the province and/or nation's social, legislative, and economic realities. Enhancing community support systems for farmers who become disabled as a result of injury or illness will require a multilevel system approach that involves health, financial, and labor resources.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".