Depressive symptoms predict medical care utilization in a population-based sample
Bibliographic record
Abstract
BACKGROUND: Several examinations have detected a relation between depressive symptoms and medical utilization. However, selection biases have been involved in most previous examinations. We sought to test the association between depressive symptoms and prospective, increased medical care utilization, in a population-based Canadian sample, while controlling for utilization due to medical illness and controlling for selection bias. METHODS: Data from the Nova Scotia Health Survey 1995, an age- and sex-stratified random sampling of 3227 Nova Scotian adults, included the Center for Epidemiological Studies-Depression scale and items assessing chronic medical conditions and current limitations in daily activities resulting from medical illness. We linked survey data with medical care utilization measures for the year following the survey, including out-patient visits, reimbursement for out-patient services, hospitalizations, and hospitalization days. RESULTS: After controlling for age, sex, count of medical diagnoses and current medical severity, those with a greater level of depressive symptoms were at greater risk of having increased medical care utilization in the following year. These results remained after removing mental health care utilization costs. CONCLUSIONS: In a population-based sample, depressive symptoms predicted greater medical care utilization, independent of a number of medical severity measures. Whether depressive symptoms are a risk marker or a causal risk factor for increased medical utilization remains to be explored.
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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 | 0.002 |
| 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 | 0.001 | 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 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".