A multidimensional tool to measure farm stressors: development and initial validation of the farmer stress assessment tool (FSAT)
Bibliographic record
Abstract
BACKGROUND: Farming is a stressful occupation, and a growing body of research shows that farm stressors are associated with poor mental health. To date, there are few methodologically sound surveys that assess farm stressors, and none have been validated for the Canadian context. Our study aimed to: (a) investigate the types of stressors experienced by farmers, (b) develop a farm stress assessment tool and test its factor structure and internal consistency, and (c) assess its criterion-related validity to self-reported levels of anxiety, depression, burnout, and resilience among farmers. METHODS: We developed a 20-item survey based on a review of the literature, examining existing farm stress surveys, and consulting 10 farmers and agricultural industry experts. Then, a convenience sample of farmers living in Alberta, Canada (Sample 1, N = 354) completed a questionnaire containing the 20-item farm stress survey and four validated measures that assessed depression, anxiety, burnout, and resilience. Sample 1 was used to assess the factor structure using exploratory factor analysis (EFA), internal consistency, and criterion-validity of the survey. Next, a convenience sample of farmers living outside of Alberta (Sample 2, N = 138) was used to evaluate the factor structure of the survey using confirmatory factor analysis (CFA). RESULTS: The results of the EFA revealed five underlying dimensions of farm stressors: Unexpected work disruptions, Agricultural hazards, Farm and financial planning, Isolation, and Regulations and public pressure. The subscales accounted for 61.6% of the variance, and the internal consistency (Cronbach's alpha) ranged from 0.66 to.75. Subscale correlations were below 0.44, indicating evidence of discriminant validity. Correlations between the five subscales and the four mental health outcome variables supported the criterion-related validity of the survey. The results of the CFA indicated that the data fit the model, and fit was further improved by correlating one pair of error terms. CONCLUSIONS: Preliminary analysis of our Farmer Stress Assessment Tool (FSAT) suggests it is a reliable and valid instrument for measuring a range of stressors farmers face. Implications for policy and community-based mental health interventions that help farmers manage the enduring stressors of agriculture is discussed.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".