Feasibility and acceptability of patient partnership to improve access to primary care for the physical health of patients with severe mental illnesses: an interactive guide
Bibliographic record
Abstract
INTRODUCTION: Even in countries with universal healthcare systems, excess mortality rates due to physical chronic diseases in patients also suffering from serious mental illness like schizophrenia is such that their life expectancy could be lessened by up to 20 years. The possible explanations for this disparity include: unhealthy habits (i.e. smoking; lack of exercise); side-effects of psychotropic medication; delays in the detection or initial presentation leading to a more advanced disease at diagnosis; and inequity of access to services. The main objective of this paper is to explore the feasibility and acceptability of patient partnership for developing an interactive guide to improve access to primary care providers for chronic diseases management and health promotion among patients with severe mental illnesses. METHODS: A participatory action research design was used to engage patients with mental illness as full research partners for a strategy for patient-oriented research in primary care for persons with schizophrenia who also have chronic physical illnesses. This strategy was also developed in partnership with a health and social services centre responsible for the health of the population of a territory with about 100,000 inhabitants in East-end Montreal, Canada. A new interactive guide was developed by patient research partners and used by 146 participating patients with serious mental illness who live on this territory, for them to be better prepared for their medical appointment with a General Practitioner by becoming more aware of their own physical condition. RESULTS: Patient research partners produced a series of 33 short videos depicting signs and symptoms of common chronic diseases and risk factors for the leading causes of mortality and study participants were able to complete the corresponding 33-item questionnaire on an electronic touch screen tablet. What proved to be most relevant in terms of interactivity was the dynamic that has developed among the study participants during the small group learning sessions, a training technique designed for healthcare professionals that was adapted for this project for, and with patient partners. CONCLUSION: This research has shown the feasibility and acceptability of patient partnership and patient-oriented research approaches to the R&D process of a new medical tool and intervention for patients with serious mental illness, and its acceptability for addressing inequity of this disadvantaged population in terms of access to primary care providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".