Quality of online physical activity information for long-haul truck drivers
Bibliographic record
Abstract
Purpose – Most long-haul truck drivers are physically inactive. Despite being identified as a source of health information, online physical activity and exercise information has not been evaluated for this population. The purpose of this paper is to evaluate the accessibility, accuracy, technical and theoretical quality, and readability of online physical activity, exercise, and sport information for long-haul truck drivers. Design/methodology/approach – A standardized protocol was followed to identify and evaluate web sites. Web sites were included in the review if they met the following criteria: first, presented information on physical activity, exercise, or sport; second, provided information for long-haul truck drivers; and finally, provided information in English. Each web site was evaluated independently by the two study authors. After evaluating the web sites independently, the authors then met to discuss each construct for each web site. Findings – Overall, 44 web sites were reviewed. Nine web sites provided information based on physical activity guidelines. Most web sites scored poorly on technical and theoretical quality. In total, 28 web sites provided information that was written above the recommended grade 8 reading level. Research limitations/implications – Research has shown that theoretically designed physical activity and exercise interventions are more successful than those with no theoretical underpinnings. Creating web sites or online applications using behavioral theory and improving the readability of online health information may help increase levels of physical activity and improve overall health for this population. Originality/value – No previous research has examined the quality of online physical activity, exercise, or sport information for long-haul truck drivers. This is the first study to examine how online health information for this population can be improved.
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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 | 0.031 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".