Inequities in Medical Follow-Up for Depression: A Population-Based Study in Montreal
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to measure the quality of medical follow-up of depression in a universal-access health care system and identify its main correlates. METHODS: This retrospective cohort study of adult patients who received diagnoses of depression between April 2003 and March 2005 in Montreal used administrative data to measure the quality of medical follow-up within the first three months of diagnosis. Indicators of adequate follow-up care included having at least one outpatient visit to a family physician or a psychiatrist, the first follow-up visit within 30 days of diagnosis, more than half of follow-up visits with the same physician, and at least three follow-up visits. RESULTS: During the study period, 41,375 Montrealers aged 18 and older received a new diagnosis of depression. Among those, 90% (N=37,071) had at least one visit with a physician (family practitioner or psychiatrist) within the first three months of diagnosis, 59% (N=24,295) benefited from continuity with their usual provider, 50% (N=20,846) received a prompt follow-up visit, and 48% (N=19,819) had optimal contacts with practitioners. Medical follow-up was less adequate for older patients, male patients, patients living in very deprived neighborhoods, and patients with high morbidity levels. The quality of medical follow-up was better when both a family physician and a psychiatrist were involved. CONCLUSIONS: The results suggest that universal access facilitates optimal practitioner contacts during the acute treatment phase of depression. However, despite universal access, the findings revealed that some inequities persist.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".