The experience and impact of chronic disease peer support interventions: A qualitative synthesis
Bibliographic record
Abstract
OBJECTIVE: Our aim was to synthesise qualitative literature about the perceived impact and experience of participating in peer support interventions for individuals with chronic disease. METHODS: We carried out a meta-ethnography to synthesize 25 papers meeting specific inclusion criteria. RESULTS: Thirteen concepts were identified that reflected participants' perceptions of the experience and impact of intervention participation. These were brought together in a conceptual model that highlighted both positive and negative perceptions, while also indicating if specific experiences and impacts had greater pertinence for mentors, mentees, or were mutually experienced. CONCLUSION: Although peer support interventions may establish uneven power relationships between mentors and mentees, there is also potential for initially asymmetrical relationships to become more symmetrical over time. Our synthesis suggests that emotional support is particularly valued when delivered under conditions that do not merely reproduce biomedical hierarchies of power. PRACTICE IMPLICATIONS: This synthesis suggests that those developing and implementing peer support interventions need to be sensitive to their potential negative effects. They will need to manage the tension between the hierarchical and egalitarian aspects of peer support interventions, and consider the impact on both mentors and mentees.
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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 | 0.034 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".