Posttraumatic growth and recovery following a first episode of psychosis: a narrative review of two concepts
Bibliographic record
Abstract
A first episode of psychosis is often a traumatic experience that leads to significant life disruptions. However, many young people recover following a first episode of psychosis. Two types of recovery from psychosis have been described in the literature: clinical recovery (i.e. the resolution of symptoms and resumption of social, occupational or educational goals) and personal recovery (i.e. finding a way to live a meaningful life despite the limitations of having a mental illness). Further, some young people may experience posttraumatic growth (i.e. positive psychological changes following the struggle with psychosis). It is unclear how posttraumatic growth and recovery are similar or distinct. This conceptual ambiguity may limit both theoretical and empirical work in the field. The purpose of this narrative review is to help resolve this ambiguity by describing similarities and differences in the historical roots, phenomenology, and predictors of recovery (both clinical and personal) and posttraumatic growth within the context of a first episode of psychosis. Our review concludes that personal recovery may be best understood as a broader construct under which clinical recovery is subsumed, and that posttraumatic growth may be a phenomenon that is related to, yet distinct from, personal recovery. Future empirical studies are needed to disentangle these phenomena.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".