Exploring the physician assistant-psychiatrist supervisory relationship and practice model at the Crisis Response Centre
Bibliographic record
Abstract
Introduction: 1 in 3 Manitobans will be faced with a mental illness in their lifetime.1 Given the limited number of psychiatrists in Manitoba, coupled with the high burden of mental illness in the population, physician assistants (PA) represent a critical resource in ensuring access to specialty psychiatric services. The Crisis Response Centre (CRC) is an innovative and resourceful 24/7 central access point for mental health services.2 This study aims to determine how to best utilize PAs in practice at the CRC and ensure continued quality improvement in our healthcare system. Methods: This study examined the current supervisory practice model using an online survey distributed to both PAs and psychiatrists currently employed at the CRC. The survey focused on: 1. Gaining an understanding of the level of comfort in the current model from both psychiatrist and PA perspectives, 2. Identifying the roles and responsibilities that could be safely added if there was a change to practice, and 3. Examining PA- and psychiatrist-factors that influenced their level of comfort in the supervisory model. Results: Ninety-seven percent of psychiatrists agree that the presence of PAs has improved overall patient care at the CRC. There appears to be significant comfort under the current practice model from both PAs and psychiatrists and an evident willingness for PAs to have further autonomy, increased roles, and responsibilities. All PAs supported a model of only reviewing cases with which they are unsure of their management. Conclusion: Optimizing PA autonomy at CRC supports the community by utilizing the practice model to increase access to care and further this reach. It supports the growth of the PA model in psychiatry locally and nationally, as the CRC site is the principal employer of PAs in mental health in the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".