Knowledge translation to fitness trainers: A systematic review
Bibliographic record
Abstract
BACKGROUND: This study investigates approaches for translating evidence-based knowledge for use by fitness trainers. Specific questions were: Where do fitness trainers get their evidence-based information? What types of interventions are effective for translating evidence-based knowledge for use by fitness trainers? What are the barriers and facilitators to the use of evidence-based information by fitness trainers in their practice? METHODS: We describe a systematic review of studies about knowledge translation interventions targeting fitness trainers. Fitness trainers were defined as individuals who provide exercise program design and supervision services to the public. Nurses, physicians, physiotherapists, school teachers, athletic trainers, and sport team strength coaches were excluded. RESULTS: Of 634 citations, two studies were eligible for inclusion: a survey of 325 registered health fitness professionals (66% response rate) and a qualitative study of 10 fitness instructors. Both studies identified that fitness trainers obtain information from textbooks, networking with colleagues, scientific journals, seminars, and mass media. Fitness trainers holding higher levels of education are reported to use evidence-based information sources such as scientific journals compared to those with lower education levels, who were reported to use mass media sources. The studies identified did not evaluate interventions to translate evidence-based knowledge for fitness trainers and did not explore factors influencing uptake of evidence in their practice. CONCLUSION: Little is known about how fitness trainers obtain and incorporate new evidence-based knowledge into their practice. Further exploration and specific research is needed to better understand how emerging health-fitness evidence can be translated to maximize its use by fitness trainers providing services to the general public.
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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 | 0.055 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".