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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".