Beyond Treatment and Diagnosis: Investigating the Depth and Geographic Distribution of Research on Holistic Recovery for First-Episode Psychosis Patients
Bibliographic record
Abstract
First-Episode Psychosis (FEP) refers to individuals early in the course of a psychotic illness or treatment for psychotic symptoms. This intervention period is especially critical in supporting the clinical recovery of patients. However, there is limited research examining non-clinical topics, such as social functioning, quality of life, employment, and social inclusion, for individuals treated for FEP in the United States. Understanding non-clinical impairments, behaviors, and life outcomes is crucial for enhancing comprehensive care that supports the overall reintegration of patients into society. This descriptive review aims to identify geographic trends in how these factors have been studied in patients treated for First-Episode Psychosis (FEP). A search strategy was developed using a search of the term "First-Episode Psychosis" on Google Scholar to gather the 500 most relevant results, irrespective of researcher nationality or geographical context. Following this, studies addressing non-clinical life outcomes were categorized into 8 subtopics and analyzed based on geographic distribution between these categories. The results suggest that the United States lags behind countries like the United Kingdom, Australia, and Canada in non-clinical first-episode psychosis (FEP) research. Strengthening U.S. research in these domains could drive policy change by demonstrating service needs, potentially justifying further expansion of holistic care models like Coordinated Specialty Care (CSC)
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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.036 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".