Contextual Factors and Programme Theories Associated with Implementing Blue Prescription Programmes: A Systematic Realist Review
Bibliographic record
Abstract
Nature-based social prescribing such as “blue prescription” promotes public health and health improvement of individuals with long-term health conditions. However, there is limited evidence explaining the relationship of contexts, mechanisms, and outcomes of implementing blue prescription programmes (BPPs) in health and social care settings that could inform policy and practice. We conducted a systematic realist review by searching PubMed, Web of Science, PsycInfo, Scopus, MEDLINE, and CINAHL for articles published in English between January 2000 and June 2022 about health and social care professionals providing referral to or prescription of blue space activities (e.g., swimming, fishing, surfing, etc.) with health-related outcomes. Components and descriptions of BPP implementation were extracted and used to develop themes of contextual factors used to develop programme theories and a logic model demonstrating the mechanisms of BPP implementation. Sixteen studies with adequate to strong quality were included from 8,619 records. After participating in BPPs referred to or prescribed by health and social care professionals, service users had improvements in their physical, cognitive (mental), social health, and proenvironmental knowledge. Service user-related contextual factors were referral information, free equipment, transportation, social support, blue space environments, and skills of service providers. Programme-related contextual factors were communication, multistakeholder collaboration, financing, and adequate service providers. Programme theories on service user enrolment, engagement, adherence, communication protocols, and programme sustainability explain the mechanisms of BPP implementation. BPPs could promote health and wellbeing if contextual factors and programme theories associated with service users’ characteristics and programme delivery are considered in the design, delivery, and evaluation of BPPs. Our study was registered with PROSPERO (CRD42020170660).
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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 | 0.037 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".