Understanding the role of the family physician in early psychosis intervention
Bibliographic record
Abstract
BACKGROUND: The family physician is key to facilitating access to psychiatric treatment for young people with first-episode psychosis, and this involvement can reduce aversive events in pathways to care. Those who seek help from primary care tend to have longer intervals to psychiatric care, and some people receive ongoing psychiatric treatment from the family physician. AIMS: Our objective is to understand the role of the family physician in help-seeking, recognition and ongoing management of first-episode psychosis. METHOD: We will use a mixed-methods approach, incorporating health administrative data, electronic medical records (EMRs) and qualitative methodologies to study the role of the family physician at three points on the pathway to care. First, help-seeking: we will use health administrative data to examine access to a family physician and patterns of primary care use preceding the first diagnosis of psychosis; second, recognition: we will identify first-onset cases of psychosis in health administrative data, and look back at linked EMRs from primary care to define a risk profile for undetected cases; and third, management: we will examine service provision to identified patients through EMR data, including patterns of contacts, prescriptions and referrals to specialised care. We will then conduct qualitative interviews and focus groups with key stakeholders to better understand the trends observed in the quantitative data. DISCUSSION: These findings will provide an in-depth description of first-episode psychosis in primary care, informing strategies to build linkages between family physicians and psychiatric services to improve transitions of care during the crucial early stages of psychosis. DECLARATION OF INTEREST: None.
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 | 0.013 | 0.041 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".