Post-traumatic Growth in Psychosis: A Systematic Review and Narrative Synthesis
Bibliographic record
Abstract
Abstract Background and Objective: People with psychosis report experiences of highly traumatic events. Positive change or post-traumatic growth (PTG) can occur as a result of traumatic experiences. Yet there is limited attention on PTG in psychosis, possibly due to the negative impact of psychotic symptoms on functioning and quality of life. The aim of this review was to identify relationships between PTG and psychosis and to develop a conceptual framework synthesising facilitators of PTG in psychosis.Method: Ten electronic databases were searched in seven languages, whilst five journals and grey literature were searched in English. Quantitative studies were eligible if examining the association or temporal relationship between PTG and one or more variables. Qualitative studies were eligible if describing PTG arising from experiences of psychosis. Findings from quantitative papers were grouped by their analysis method, whilst a narrative synthesis was conducted of findings in qualitative papers.Results: 37 papers were included. Mediators of PTG in psychosis included meaning in life, coping self-efficacy, core beliefs, and self-reported recovery. Seven facilitators of PTG in psychosis were identified: Personal identity and strength, Receiving support, Opportunities and possibilities, Strategies for coping, Perspective shift, Emotional experience, and Relationships, giving the acronym PROSPER.Conclusions: Individuals with psychosis can grow from traumatic experiences. Clinicians can support PTG through the provision of trauma-informed care that supports positively valued identity changes. For researchers, the findings provide an evidence-based theoretical framework for conceptualising PTG, which can be validated through longitudinal cohort studies and underpin the development of new clinical interventions.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".