Comprehensiveness of care for women with depression
Bibliographic record
Abstract
OBJECTIVE: To explore comprehensiveness of care in patients with depression by examining associations between a diagnosis of depression, frequency of primary care visits, and Papanicolaou test completion. DESIGN: Cross-sectional retrospective survey using electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network. SETTING: Primary care practices in Ontario. PARTICIPANTS: Women aged 21 to 69 eligible to receive Pap tests in 2015. MAIN OUTCOME MEASURES: Associations between 2 predictors (depression and number of primary care visits in 2015) and Pap test completion were measured. RESULTS: Overall, 125,258 women were included: 20.5% completed a Pap test and 16.4% had a diagnosis of depression. Having a diagnosis of depression was associated with lower likelihood of Pap test completion (adjusted odds ratio [AOR]=0.92, 95% CI 0.88 to 0.95). A greater number of primary care visits was associated with a higher likelihood of Pap test completion; this association was stronger in women with a diagnosis of depression (AOR=4.9, 95% CI 4.16 to 5.69) than in those without (AOR=3.4, 95% CI 3.25 to 3.60). CONCLUSION: While depression was associated with fewer completed Pap tests, women with depression who saw their family doctors more often were more likely to be screened for cervical cancer. More primary care visits for depression treatment may be associated with an improved likelihood of screening for cervical cancer.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".