How do recorded mental health recovery narratives create connection and improve hopefulness?
Bibliographic record
Abstract
BACKGROUND: Mental health recovery narratives are an active ingredient of recovery-oriented interventions such as peer support. Recovery narratives can create connection and hope, but there is limited evidence on the predictors of impact. AIMS: The aim of this study was to identify characteristics of the narrator, narrative content and participant which predict the short-term impact of recovery narratives on participants. METHOD: = 13). In both studies, participants with mental health problems received recorded recovery narratives and rated impact on hopefulness and connection. Predictive characteristics were identified using multi-level modelling. RESULTS: The experimental study found that narratives portraying a narrator as living well with mental health problems that is intermediate between no and full recovery, generated higher self-rated levels of hopefulness. Participants from ethnic minority backgrounds had lower levels of connection with narrators compared to participants from a white background, potentially due to reduced visibility of a narrator's diversity characteristics. CONCLUSIONS: Narratives describing partial but not complete recovery and matching on ethnicity may lead to a higher impact. Having access to narratives portraying a range of narrator characteristics to maximise the possibility of a beneficial impact on connection and hopefulness.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".