Peer support programs in the fields of medicine and nursing: a systematic search and narrative review
Bibliographic record
Abstract
Peer-provided services exist in many different domains and professions. However, there is a knowledge gap in the existing programs' descriptions and grouping that hinders creating new high-quality peer support programs. The objectives of this article are two-fold in describing existing peer support programs published in the literature in the medical field and evaluating their descriptive quality. Six electronic databases, grey literature, and reference lists were systematically searched. Studies reporting the existence of a support program delivered by peers and its description or methodology were included. Studies targeting patients and children were excluded. 11 articles were included in the qualitative synthesis and explored in detail. A total of 2155 peers participated in support programs in the fields of medicine, nursing, or both. Programs in other professional fields were not found. Programs were described in five different countries. Three methods of peer support delivery were found: in person, online, and mixed varying in their goals, duration, peer training supervision and participant demographics and number. Program descriptions were rated as good, fair, or poor using a verified rating scale. There are numerous well-described programs varying in their methodology and type of delivery. Thus, the emergence of new programs can be based on such models that have been well-described in the literature.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".