The diagnosis of depression and its treatment in Canadian primary care practices: an epidemiological 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: A diagnosis of depression is common in primary care practices, but data are lacking on the prevalence in Canadian practices. We describe the prevalence of the diagnosis among men and women, patient characteristics and drug treatment in patients diagnosed with depression in the primary care setting in Canada. METHODS: Using electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network, we examined whether the prevalence of a depression diagnosis varied by patient characteristics, the number of chronic conditions and the presence of the following chronic conditions: hypertension, diabetes, chronic obstructive pulmonary disease, osteoarthritis, dementia, epilepsy and parkinsonism. We used regression models to examine whether patient characteristics and type of comorbidity were associated with a depression diagnosis. RESULTS: Of the 304 412 patients who had at least 1 encounter with their primary care provider between Jan. 1, 2011, and Dec. 31, 2012, 14% had a diagnosis of depression. Current or past smokers and women with a high body mass index had higher rates of depression. One in 4 patients with a diagnosis of depression also had another chronic condition; those with depression had 1.5 times more primary care visits. About 85% of patients with depression were prescribed medication, most frequently selective serotonin reuptake inhibitors, followed by atypical antipsychotics. INTERPRETATION: Our data provide information on the prevalence of a depression diagnosis in primary care and associations with being female, having a chronic condition, smoking history and obesity in women. Our findings may inform research and assist primary care providers with early detection and interventions in at-risk patient populations.
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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 | 0.001 | 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) | 0.000 | 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