An exploration of meaningful involvement in ropes course programs
Bibliographic record
Abstract
Ropes course programs provide numerous benefits but what makes programs effective has been unclear. The purpose of the study was to: 1) determine if there was a measurable difference in meaningful involvement between the Challenge by Choice (CbC) and Inviting Optimum Participation (IOP) approaches to ropes course program design and delivery, and 2) identify and compare the linkages among program attributes, outcomes, and values with each approach. The study involved 360 young adolescents (ages 10 -15) who took part in full-day ropes course programs provided by one of four organizations in Ontario, Canada. Participants included 172 boys and 188 girls. The average age was 12.7 years and 47% had previous ropes course program experience. Participants completed either: 1) an experience sampling survey related to meaningful involvement or 2) a means-end laddering survey related to program attributes, outcomes, and values. The experience sampling data were subjected to multivariate analysis of variance (MANOVA). The dependent variables were meaningful involvement during high (belayed) activities and low (non-belayed) activities. The independent variables were program type, sex, and ropes course experience. Program type had a significant effect (8(3,158) = 0.937, p< 0.05) for meaningful involvement experienced during high activities. Meaningful involvement consists of the areas engagement, choice, and view of self. Follow-up ANOVAs indicated that choice (F(1, 160) = 6.127, p< 0.05) was significantly higher in IOP programs. Differences between the programs in engagement and view of self were not significant. There was no significant effect for low activities. Means-end analysis was used to identify linkages among program attributes, outcomes, and values. Responses were organized into 11 Hierarchical Value Maps. Participants in IOP programs identified different linkages than participants in CbC programs. IOP program participants were more likely to mention the attribute low activities and the outcome group efficacy as significant and less likely to mention the outcome anxiety. CbC participants were less likely to mention low activities and group efficacy and more likely to mention anxiety and the value excitement. These findings indicate that ropes course program design and delivery can be manipulated to provide specific benefits and facilitate participant experiences of meaningful involvement.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".