Understanding Engagement with a Physical Health Service: A Qualitative Study of Patients with Severe Mental Illness
Bibliographic record
Abstract
OBJECTIVE: Individuals with severe mental illness (SMI) are disproportionally affected by medical comorbidities, resulting in poor physical health and premature death. Despite this, care for chronic medical conditions is suboptimal, and there is limited research that explores this phenomenon from the patient's perspective. The aim of this study was to identify barriers and facilitators of engagement with a physical health service experienced by individuals with SMI. METHODS: Adults with SMI were recruited from a large psychiatric hospital and offered referral to a physical health service focused on the prevention and treatment of obesity and diabetes. Interviews were conducted at referral, 3, and 6 months. Data from 56 interviews of 24 participants were analyzed using the framework method to identify factors influencing engagement. RESULTS: Barriers to engagement were identified at individual, medical program, and health system levels. Factors influencing the individual experience included difficulty in care coordination, affective symptomatology, and ability to bond with providers. Factors at the program level included difficulty adjusting to the clinic environment and the inability to achieve treatment goals. Factors at the system level included challenges in attending multiple appointments in a fragmented health system, lack of social support, and financial constraints. CONCLUSIONS: This qualitative study suggests that traditional models of medical care for chronic conditions pose challenges for many individuals with SMI and contribute to health disparities. Adaptation of medical care to populations with SMI and close collaboration between medical and mental health services are necessary to improve medical care and, subsequently, health outcomes.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".