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Record W4401521963 · doi:10.1186/s40359-024-01929-w

A multidimensional tool to measure farm stressors: development and initial validation of the farmer stress assessment tool (FSAT)

2024· article· en· W4401521963 on OpenAlexaffabout
Rebecca J. Purc‐Stephenson, S. Dedrick, Darryl B. Hood

Bibliographic record

VenueBMC Psychology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCronbach's alphaStressorPsychologyConfirmatory factor analysisExploratory factor analysisAnxietyBurnoutSample (material)Context (archaeology)Applied psychologyClinical psychologyPsychometricsStructural equation modelingStatisticsGeographyPsychiatry

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.311
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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