The motivations, interests and concerns of prospective peer leaders of nature-based mental health interventions
Bibliographic record
Abstract
Abstract Background There is renewed, cross-sectoral interest in nature's contributions to health and how they can be maximised for public and planetary health. Current enquiry is focused on practicalities and what strategies and collaborations are needed to operationalise the nature-health relationship for both people and the environment. Nature-based health interventions (NBIs), especially those within the framework of nature-based social prescribing (NBSP), show promise. However, in the UK, there are limitations to initiating and scaling activities due to issues like: navigating multi-stakeholder partnerships, building a work force and sustaining the provision. The short ‘course’, finite format of NBSP programmes also creates a ‘what next’ moment for participants despite willingness and interest from many participants in peer leadership type activity i.e. offering social support and leadership to help others in their community to partake in nature-based health activities. Aim: To explore peer-led community delivery options for NBIs with prospective peer leaders. Method: We recruited seventeen study participants for a daylong workshop exploring motivations, activities and perceived challenges of prospective peer leaders of NBIs. The study participants had a) been through, or supported delivery/participation of, a UK wetland-based NBSP programme for poor mental health and b) expressed an interest in peer leadership activities. The data underwent reflexive thematic analysis. Results. Motivations of prospective peer leaders to offer nature-based health activities related to the promotion of personal and community wellbeing through learning, sharing nature experiences, creating social connections, and through interests in facilitating a wide range of nature-based and salutogenic activities (e.g. arts/creative, conservation, nature appreciation, mental wellbeing activities). Concerns from peer leaders centred on the practicalities of establishing and safely delivering nature-based activity, on the personal competencies required to deliver NBIs, nervousness with the medicalised nature of social prescribing, and on resource needs for delivery; the latter highlighted the importance of local nature provision for community-led NBI delivery. Conclusions: Community-led NBIs offer potential to broaden public health options, but community concerns need to be addressed first. There is a role for allied NGO organisations, or social prescribing networks, to share resources and support communities and prospective peer leaders to overcome these concerns.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".