Major depression, fibromyalgia and labour force participation: A population-based cross-sectional study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Previous studies have documented an elevated frequency of depressive symptoms and disorders in fibromyalgia, but have not examined the association between this comorbidity and occupational status. The purpose of this study was to describe these epidemiological associations using a national probability sample. METHODS: Data from iteration 1.1 of the Canadian Community Health Survey (CCHS) were used. The CCHS 1.1 was a large-scale national general health survey. The prevalence of major depression in subjects reporting that they had been diagnosed with fibromyalgia by a health professional was estimated, and then stratified by demographic variables. Logistic regression models predicting labour force participation were also examined. RESULTS: The annual prevalence of major depression was three times higher in subjects with fibromyalgia: 22.2% (95% CI 19.4 - 24.9), than in those without this condition: 7.2% (95% CI 7.0 - 7.4). The association persisted despite stratification for demographic variables. Logistic regression models predicting labour force participation indicated that both conditions had an independent (negative) effect on labour force participation. CONCLUSION: Fibromyalgia and major depression commonly co-occur and may be related to each other at a pathophysiological level. However, each syndrome is independently and negatively associated with labour force participation. A strength of this study is that it was conducted in a large probability sample from the general population. The main limitations are its cross-sectional nature, and its reliance on self-reported diagnoses of fibromyalgia.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it