“I'm not too old to lift”: exploring lifelong involvement in Olympic weightlifting through the serious leisure perspective
Bibliographic record
Abstract
Introduction: This study explores the lifelong involvement of older adults in Olympic weightlifting (OW) with the aim of understanding the factors that motivate them to initiate and sustain participation across their lifespan, as well as the challenges they face and the benefits they experience. Methods: Semi-structured in-depth interviews were conducted with 22 participants (18 males, 4 females), aged 50-89, who had over 30 years of training experience in OW. The interviews lasted 40-58 min and were conducted face-to-face or via video call. The data was framed through Stebbins 'serious leisure perspective with an inductive thematic analysis to identify themes. Results and discussion: Five themes and four subthemes were identified that shape participants' enduring involvement in OW: lifelong passion and commitment to OW (subthemes: perseverance and serious leisure career development), rigorous training regimens, injury experiences and recovery, self-improvement and personal growth, and social and community building (subthemes: community and camaraderie, coaching and mentorship for athlete development and legacy). These themes reveal the deep dedication, resilience, and strong sense of community that mark participants' enduring involvement in this sport. The results suggest that OW can be utilized as a valuable sport for healthy aging, personal growth, and building supportive networks, which can inform approaches in health promotion, fitness programming, and sport development across different age groups.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".