Health Related Quality of Life of Off-Road Vehicle Riders
Bibliographic record
Abstract
Background: Alternative forms of physical activity are becoming increasingly popular, but little is know about the effects of habitual involvement in these activities on health-related quality of life (QOL). Purpose: The purpose of this study was to characterize the heath-related QOL of Canadians who habitually participate in recreational off-road vehicle riding. A secondary purpose was to compare the levels of mental and physical functioning QOL of recreational off-road vehicle riders to Canadian population norms and determine whether differences exist among genders, age categories and vehicle types. Methods: Habitual participants in recreational off-road vehicle riding were questioned regarding QOL using the Medical Outcomes Study SF-36 questionnaire. Responses were compared between groups and to normative data. Results: SF-36 mental function (54.5) and physical function (55.4) scores of off-road riders were higher than Canadian norms (51.7 and 50.5, respectively). Conclusions: Off-road riders have high levels of mental and physical functioning QOL. Given their higher physical function, off-road motorcycle riders are less likely than all terrain vehicle riders or the general population to have physical limitations or health problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".