parkrun participation, impact and perceived social inclusion among runners/walkers and volunteers with mental health conditions
Bibliographic record
Abstract
Engagement in recreation can positively impact the physical and mental health of those experiencing mental health challenges; however, the impact of engaging in other aspects of such recreation, such as volunteering, remain largely unexplored in this population. Volunteering is known to have a wealth of health and wellbeing benefits among the general population; therefore, the impact of recreational-based volunteering for those with mental health conditions deserves to be explored. The current study sought to examine the health, social and wellbeing impacts of parkrun engagement among runners and volunteers living with a mental health condition. Participants with a mental health condition (N = 1661, M(SD)age = 43.4 (12.8) years, 66% female) completed self-reported questionnaires. A MANOVA was conducted to examine the differences in health and wellbeing impacts between those who run/walk vs. those who run/walk and volunteer, while chi-square analyses examined variables of perceived social inclusion. Findings suggest that there was a statistically significant multivariate effect of participation type on perceived parkrun impact (F (10, 1470) = 7.13; p < 0.001; Wilk’s Λ = 0.954, partial η2 = 0.046). It was also found that for those who run/walk and volunteer, compared to those who only run/walk, parkrun made them more feel part of a community (56% v 29% respectively, X2(1) = 116.70, p < 0.001) and facilitated them meeting new people (60% v 24% respectively, X2 (1) = 206.67, p < 0.001). These results suggest that the health, wellbeing, and social inclusion benefits of parkrun participation are different for those who run and volunteer, compared to those who only run. These findings may have public health implications and clinical implications for mental health treatment, as they convey that it is not simply the physical engagement in recreation that may play a role in one’s recovery, but also the volunteer aspect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".