Primary care for individuals with serious mental illness (PriSMI): protocol for a convergent mixed methods study
Bibliographic record
Abstract
INTRODUCTION: People with serious mental illness (SMI) have poor health outcomes, in part because of inequitable access to quality health services. Primary care is well suited to coordinate and manage care for this population; however, providers may feel ill-equipped to do so and patients may not have the support and resources required to coordinate their care. We lack a strong understanding of prevention and management of chronic disease in primary care among people with SMI as well as the context-specific barriers that exist at the patient, provider and system levels. This mixed methods study will answer three research questions: (1) How do primary care services received by people living with SMI differ from those received by the general population? (2) What are the experiences of people with SMI in accessing and receiving chronic disease prevention and management in primary care? (3) What are the experiences of primary care providers in caring for individuals with SMI? METHODS AND ANALYSIS: We will conduct a concurrent mixed methods study in Ontario and British Columbia, Canada, including quantitative analyses of linked administrative data and in-depth qualitative interviews with people living with SMI and primary care providers. By comparing across two provinces, each with varying degrees of mental health service investment and different primary care models, results will shed light on individual and system-level factors that facilitate or impede quality preventive and chronic disease care for people with SMI in the primary care setting. ETHICS AND DISSEMINATION: This study was approved by the University of Ottawa Research Ethics Board and partner institutions. An integrated knowledge translation approach brings together researchers, providers, policymakers, decision-makers, patient and caregiver partners and knowledge users. Working with this team, we will develop policy-relevant recommendations for improvements to primary care systems that will better support providers and reduce health inequities.
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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 | 0.133 | 0.086 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.102 | 0.020 |
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".