How Do Master Weightlifters Train? A Transnational Study of Weightlifting Training Practices and Concurrent Training
Bibliographic record
Abstract
(1) Background: The wide range of preparedness, physical fitness, and capabilities of older athletes makes it challenging to recommend general training programs for this cohort ranging from ages 35 to 80 and older. Weightlifting has enjoyed an unprecedented growth in recent years, especially among women. The objectives of this study are to describe age and sex differences in self-reported training regimens and concurrent training for Masters weightlifters and investigate regional differences. (2) Methods: A total of 1051 Masters weightlifters from Australia, Canada, Europe, and the USA completed an online survey that included questions on sport history and training practices. (3) Results: A training session typically lasted 1.5 to 2 h for both sexes across all ages. Weightlifters engaged in concurrent training (66.9%), especially in endurance training (24.9%) and CrossFit (36.4%), but the proportions differed across geographic regions. Older females maintained training 4 days per week, while older males reduced this to 3 days per week. (4) Conclusions: Weightlifting training practice of Masters athletes was remarkably consistent across ages, but concurrent training differed between males and females and across regions. This study provides helpful information for athletes, coaches, and sport organizations about the variation in weightlifting training practices and concurrent training of older athletes.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".