Predictors of Physician Follow-Up Care Among Patients Affected by an Incident Mental Disorder Episode in Quebec (Canada): Prédicteurs des soins du suivi médical des patients affectés par un épisode incident de troubles mentaux au Québec (Canada)
Bibliographic record
Abstract
Objectives This study identified predictors of prompt (1+ outpatient physician consultations/within 30 days), adequate (3+/90 days) and continuous (5+/365 days) follow-up care from general practitioners (GPs) or psychiatrists among patients with an incident mental disorder (MD) episode. Methods Study data were extracted from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), which covers 98% of the population eligible for health-care services under the Quebec (Canada) Health Insurance Plan. This observational epidemiological study investigating the QICDSS from 1 April 1997 to 31 March 2020, is based on a 23-year patient cohort including 12+ years old patients with an incident MD episode ( n = 2,670,133). Risk ratios were calculated using Robust Poisson regressions to measure patient sociodemographic and clinical characteristics, and prior service use, which predicted patients being more or less likely to receive prompt, adequate, or continuous follow-up care after their last incident MD episode, controlling for previous MD episodes, co-occurring disorders, and years of entry into the cohort. Results A minority of patients, and fewer over time, received physician follow-up care after an incident MD episode. Women; patients aged 18-64; with depressive or bipolar disorders, co-occurring MDs–substance-related disorders (SRDs) or physical illnesses; those receiving previous GP follow-up care, especially in family medicine groups; patients with higher prior continuity of GP care; and previous high users of emergency departments were more likely to receive follow-up care. Patients living outside the Montreal metropolitan area; those without prior MDs; patients with anxiety, attention deficit hyperactivity, personality, schizophrenia and other psychotic disorders, or SRDs were less likely to receive follow-up care. Conclusion This study shows that vulnerable patients with complex clinical characteristics and those with better previous GP care were more likely to receive prompt, adequate or continuous follow-up care after an incident MD episode. Overall, physician follow-up care should be greatly improved.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".