Effectiveness of health promotion programmes for truck drivers: A systematic review
Bibliographic record
Abstract
Objective: To review the characteristics of effective health promotion interventions for reducing chronic diseases and their risk factors in truck drivers. Methods: MEDLINE (PubMed), SCOPUS, Web of Science Conference Proceedings, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and the National Transportation Library were searched using keywords related to ‘truck driver’, ‘commercial driver’, and ‘health promotion’. Reference lists of relevant documents were hand-searched. Results: The search strategy identified 2,372 non-duplicate citations, of which nine met the inclusion criteria. These nine articles represented eight unique interventions. No studies measured chronic disease as an outcome. Six interventions incorporated multiple components and reported positive findings on various intermediate health outcomes (i.e. body mass index [BMI], % body fat) or health behaviours (i.e. nutrition, physical activity). The other two interventions modified work practices only, and found no significant improvements on fatigue and psychosocial measures. Conclusion: Health promotion interventions for truck drivers can improve both intermediate health outcomes and health behaviours over the short term. The small body of literature on health promotion interventions is a concern given that truck drivers are an at-risk population and their health impacts the safety of the driving public. Studies primarily focused on changes at the individual level and this is also a concern as environmental and work organisation factors are important determinants of both chronic disease outcomes and health-related behaviours in truck drivers. Future research should also include economic evaluations as well as methods to determine facilitators and barriers to programme participation and continuation.
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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".