Access to a regular primary care physician among young people with early psychosis in Ontario, Canada
Bibliographic record
Abstract
AIM: Access to a primary care physician in early psychosis facilitates help-seeking and engagement with psychiatric treatment. We examined access to a regular primary care physician in people with early psychosis, compared to the general population, and explored factors associated with access. METHODS: Using linked health administrative data from Ontario (Canada), we identified people aged 14-35 years with a first diagnosis of nonaffective psychotic disorder (n = 39 449; 2005-2015). We matched cases to four randomly selected general population controls based on age, sex, neighbourhood, and index date (n = 157 796). We used modified Poisson regression to estimate prevalence ratios (PR) for access to a regular primary care physician in the year prior to first diagnosis of psychotic disorder, and the sociodemographic and clinical factors associated with access. RESULTS: A larger proportion of people with early psychosis had a regular primary care physician, relative to the general population (89% vs. 68%; PR = 1.30, 95%CI = 1.30-1.31). However, this was accounted for by a higher prevalence of comorbidities among people with psychosis, and this association was no longer present after adjustment (PR = 0.97, 95%CI = 0.97, 0.98). People with early psychosis who were older, male, refugees and those residing in lower income or high residential instability neighbourhoods were less likely to have a regular primary care physician. CONCLUSION: Approximately one in ten young people with early psychosis in Ontario lack access to a regular primary care physician. Strategies to improve primary care physician access are needed for management of physical comorbidities and to ensure continuity of care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".