Farmer Burnout in Canada
Bibliographic record
Abstract
While farmers in several countries worldwide are reported to be at higher risk for poor mental health outcomes like chronic stress, depression, and anxiety, there is a paucity of research on burnout in farmers. This cross-sectional study used an online survey administered between September 2015 and February 2016 to investigate burnout (as measured by the Maslach Burnout Inventory-General Survey (MBI-GS)) amongst farmers in Canada. The specific objectives were to measure the three components of burnout (exhaustion, cynicism, and professional efficacy), and to explore potential associated risk factors, as well as to determine the prevalence of the different burnout profiles (engaged, ineffective, overextended, disengaged, and burnout). MBI-GS results were obtained from 1075 farmers. Approximately 70% of the study sample identified as male and 30% as female, and participants were from a variety of farming commodities. Scores for exhaustion, cynicism, and professional efficacy were all higher than international norms. While 43% of participants were classified as engaged, 44% were classified in the ineffective, overextended, or disengaged profiles (i.e., intermediate profiles on the engagement - burnout continuum), and 12% were classified in the burnout profile. Risk factor results highlighted the positive effects of farmer support from spouse/romantic partner, friends, and industry. Overall, the results from this study demonstrate cause for concern with respect to farmer burnout, suggest potential avenues for intervention, and serve as a call to action to better support farmers in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".