Mental health recovery narratives: their impact on service users and other stakeholder groups
Bibliographic record
Abstract
Purpose The purpose of this paper is twofold: first, assess the effects of the peers’ recovery narratives on service users’ perceived mental health recovery; and second, explore various stakeholders’ perspectives on the program, specifically its facilitators and barriers. Design/methodology/approach The study used a convergent mixed-method design. First, a pre-test post-test design was used with service users to evaluate the peer recovery narrative program. They completed the Recovery Assessment Scale (RAS) and participated in qualitative interviews that explored perspectives on their mental health recovery before and after the program. Second, a cross-sectional design was used to explore stakeholder groups’ perspectives on the recovery narrative program immediately after listening to the narratives. Findings While findings show that there was no statistical difference between scores on the RAS before and after the peer narratives, thematic analysis revealed a change in service users’ understanding of recovery post-narratives. Other stakeholder groups confirmed this change. However, some healthcare professionals questioned the universal positive effects of the peer recovery narrative program on service users. Stakeholders agreed that beyond effects of the peer recovery narrative program on service users, there were also positive effects among the peers themselves. Originality/value To the authors’ knowledge, this is the first Canadian study, and one of the first studies to rely on mixed-methods and various stakeholder groups to evaluate the impact of peer recovery narratives on service users. The research, thus, fills a knowledge gap on peer recovery narratives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".