Mental health in Alberta grain farmers using pesticides over many years
Bibliographic record
Abstract
BACKGROUND: Exposure to organophosphates and certain other pesticides has been related to symptoms of mental ill-health. There is particular interest in whether exposure over many years may result in chronic ill-health. AIMS: To relate lifetime history of pesticide exposure to symptoms and medical records of mental ill-health in elderly grain farmers in Alberta. METHODS: Two populations of grain farmers were identified for study: cohort A (n = 5986), members of an Alberta farm organization in 1983; cohort B (n = 4781), grain farmers registered with the provincial department of agriculture. In 2002-03 both groups completed a questionnaire on lifetime history of pesticide use, physician diagnosed disease and recent neuropsychological symptoms. Durations of exposure to seven pesticide components were calculated and a factor score extracted from responses to the symptom questionnaire. For a sub-cohort surviving to 2009 medical records for mental ill-health were identified. Records and symptom scores were related to pesticide exposures allowing for confounding. RESULTS: From cohort A, 1348 and cohort B, 1078 were alive and interviewed (median age 63 years; median duration in farming 40 years): 1371 were linked to the medical records database. Mental ill-health symptom scores were related to duration of exposure to phenoxy compounds (but not to other pesticides) and to mental ill-health in medical records. Those with mental ill-health in hospital discharge records were more likely to have been exposed to phenoxy compounds for ≥35 years. CONCLUSIONS: The relationship of long-term exposure to phenoxy herbicides and neuropsychiatric symptoms was unexpected but not explained by measured confounders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".