Profiles of physician follow-up care, correlates and outcomes among patients affected by an incident mental disorder
Bibliographic record
Abstract
OBJECTIVES: This study identified profiles of outpatient physician follow-up care and other practice features, mostly after detection of incident mental disorders (MD), and associated these profiles with patient characteristics and subsequent adverse outcomes. METHODS: A cohort of 170,957 patients age 12 + with a new or recurrent MD detected in 2019-20 was investigated based on data from the Quebec Integrated Chronic Disease Surveillance System. Latent class analysis was performed to identify follow-up care profiles, mostly within one year of MD detection. Bivariate analyses tested associations between profiles and patient characteristics; logistic regressions examined relationships between profiles and adverse outcomes after one year. RESULTS: Five profiles were identified: Profiles 2 and 5 (64%) offered low mental health (MH) outpatient follow-up care, while the others dispensed higher MH follow-up care. Profiles differed in patient characteristics and related outcomes. Labelled "Follow-up care by usual psychiatrist", Profile 1 (1% of sample) included younger patients with the most health and social issues. Profile 2 (50%), "Low MH follow-up care but high prior consultations for physical reasons", mostly integrated older patients with chronic physical illnesses. Profile 3 (11%), "Follow-up care by general practitioners (GP) and psychiatrists", referred to physicians other than the usual ones (e.g., walk-in practice) and encompassed patients with severe MD conditions. Profile 4 (23%), "High follow-up care by usual GP and prior consultations for physical reasons", showed the typical characteristics of patients treated in primary care (more common MD, women, less materially and socially deprived). Profile 5 (15%), "Low MH follow-up care and prior consultations for physical reasons", integrated more younger men, materially deprived patients, and with substance-related disorders (SRD) or co-occurring MD-SRD. More Profile 1 and 3 patients lived in university regions - those of Profile 4 were the least numerous in such regions. More Profile 5 patients lived in metropolitan and rural areas. Risk of death was higher in Profiles 5, 2, 3, and risk of frequent ED use and hospitalization higher in Profiles 1, 3, and 5 - patients with severe health and social issues. CONCLUSION: The study confirmed the need to improve prompt, adequate and continuous follow-up care for patients with incident MD.
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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 | 0.004 |
| 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 | 0.001 |
| 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".