Pathways of care for early psychosis
Bibliographic record
Abstract
Pathways of care, a set of specific steps taken in the care of a particular disorder, are rarely employed in mental health. The Early Psychosis Intervention (EPI) Programme has taken up the challenge of developing care pathways for the treatment of early psychosis. The EPI Programme provides services to clients across several communities through a team of community‐based mental health clinicians and psychiatrists. The current pathway focuses on care provided by the clinicians. The goals for the pathway include: (a) providing a practical ‘best practices’ guide to care, (b) standardizing care, (c) providing a method for evaluating and improving quality of care and (d) improving client outcome. Prior to developing the pathway, clinical practice was assessed through random chart audits and interviews with the clinicians. A pathway was then developed incorporating both outcome measurement and a documentation system utilizing a series of checklists detailing currently recognized bestpractice in early psychosis, organized according to phase ofrecovery. The pathway was piloted and revised before fullimplementation. Extension of the pathway to early psychosis inpatient treatment and psychiatric care is currently underway.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".