How do people with first episode psychosis experience therapeutic relationships with mental health practitioners? A narrative review
Bibliographic record
Abstract
Background First-episode psychosis (FEP) refers to the first time someone experiences an episode of psychosis, which can be frightening and confusing, leading people to make their first contact with early intervention services. Early intervention is widely accepted as beneficial for long-term recovery and symptom management. A universal feature of intervention is a relationship with mental health practitioners. Therapeutic relationships experienced as positive are also associated with better outcomes across mental health settings. However, little is known about what is helpful within therapeutic relationships for people with FEPMethod The current review aimed to develop a rich understanding of beneficial features of therapeutic relationships for people with FEP to enhance service delivery. Databases searched were: APA PsycInfo, MEDLINE Complete, CINAHL.Results A systematic search yielded 178 papers, of which 16 met the inclusion criteria. Publications reviewed were from Singapore, Western Finnish Lapland, England, Canada, the United States of America, Denmark, and Australia. The papers were published across 12 journals; 81% were qualitative, 12% were quantitative, and one was a mixed methods study.Discussion It is recommended that creating a safe space to talk, taking a non-judgemental approach, and developing trust between practitioner and client should be prioritised for people with FEP.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".