Assertive Community Treatment Team Members’ Mental Models of Primary Care
Bibliographic record
Abstract
People with serious mental illnesses (SMIs) (e.g., schizophrenia, major depressive disorder, bipolar disorder) receive inadequate medical care, which is associated with high rates of avoidable morbidity and premature mortality. Assertive Community Treatment (ACT) is an evidence-based service delivery model that provides intensive mental and social health support to clients with SMI. It has been suggested that ACT should provide primary care services to address client physical health, however, initiatives towards this and their implications are not well understood. I used a case study approach and semi-structured interviews to explore five ACT teams in the Ottawa region to discover team members’ mental models of primary care, relationships with external primary care providers, and the perceived impact COVID-19 has had on these mental models. I used Shared Mental Model (SMM) theory to frame data collection and a thematic analysis. The results showed that ACT team members similarly perceived primary care as important for the holistic health of their clients. They described ACT’s psychosocial scope and how they support clients’ access to external primary care services and their work to mitigate barriers. Teams did not share mental models about the basic primary care services they provided or which roles delivered them, due to differences in context and team members’ comfort. Team members also did not share beliefs about the future of ACT and primary care integration. Finally, the COVID-19 pandemic changed and challenged primary care delivery, with beliefs becoming more negative overall. This thesis provides insight into how primary care could be delivered to ACT clients and where challenges and improvements can be addressed.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".