Assertive Community Treatment Team Members’ Mental Models toward Primary Care
Bibliographic record
Abstract
Context: People with serious mental illnesses (e.g., schizophrenia, bipolar disorder) have inequitable access to primary care, which is associated with avoidable morbidity and mortality. Assertive Community Treatment (ACT) is an evidence-based model that provides intensive mental and social health support. ACT’s engagement with primary care (both in providing primary care services or collaborating with external primary care providers) is not well understood. Objective: To discover ACT team members’ mental models (i.e., psychological representations) of the provision of primary care (within the team and through collaboration with external primary care providers), and the perceived impact of COVID-19 on these mental models. Study Design and Analysis: An exploratory multiple qualitative case study using semi-structured interviews and thematic analysis. Shared Mental Model theory framed analysis. Setting or Dataset: Ontario, Canada. Population Studied: Interdisciplinary ACT team members. Results: Twenty-seven participants from 5 ACT teams in one region were interviewed, including administrators, social workers, psychiatrists, mental health workers/counsellors, occupational therapists, nurses, and a recreational therapist. ACT team members perceived that primary care was important for their clients. Some teams offered a limited set of medical primary care services to meet clients’ needs. Most participants did not think that ACT team mandates should expand to include primary care. They should instead support collaboration with clients’ external primary care providers, as this enables client integration into the wider community. To liaise with external providers, ACT team members reported that they must navigate barriers at multiple levels (i.e., client, provider, and system levels). Most participants believed the COVID-19 pandemic delayed client access to primary care, demanded more time and risk exposure from ACT to support care, and shifted to virtual care without considering all clients’ needs. Some teams reported an increase in internal primary care provision during the pandemic. This was associated with burnout and reinforced the importance of external primary care provision. Conclusions: Findings provide insight into the different ways primary care can be delivered to ACT clients, which could provide important lessons for ACT teams in North America.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".