Trekstock Meet & Move: The Impact of One-Day Health and Well-Being Events for Young Adults with Cancer
Bibliographic record
Abstract
Purpose: To evaluate the impact of a series of one-day events delivered by Trekstock, a charity supporting young adults with cancer in the United Kingdom. Methods: Data on physical activity, mood, perceived support, self-efficacy, and confidence to be active were collected at three time points: before, after, and 2 weeks following the Meet & Move events. Results: Ninety-seven young adults with cancer (mean age: 29 years, 35% still receiving active treatment) attended a Trekstock Meet & Move event and participated within the evaluation. Baseline data demonstrated that before attending a Meet & Move event, 27% ( n = 23) of young adults reported feeling their cancer excluded them from engaging in exercise, 44% ( n = 37) reported concern that exercise will cause pain or injury, and only 38% ( n = 32) knew what exercise they could do. Data collected post-event and at follow-up indicated that Meet & Move had a positive impact upon attendees' self-efficacy and confidence to be active with more than half reporting they felt inspired after attending. There was also a significant reduction in reported worry that exercise may cause pain or injury and reported perception of feeling left out of exercise because of cancer ( p < 0.05). Following engagement in the Meet & Move events, 45% of attendees had either signed up for an additional Trekstock physical activity program or initiated engagement in a new type of physical activity on their own. Conclusion: Trekstock Meet & Move events inspire and motivate young adults with cancer in their 20s and 30s to be active.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".