‘If you’ve got a fire, you can rekindle it’: Learning how to re-engage in sport following a lapse through a multiple-case study of Masters athletes
Bibliographic record
Abstract
The COVID-19 pandemic led to the implementation of socio-sanitary restrictions and a mass exodus from organized sport and physical activity (PA). As restrictions are lifted, many individuals are attempting to re-engage with sport. Masters athletes (MAs) are optimal for studying successful re-engagement because they adhere to sport throughout adulthood. 'Rekindlers' are MAs who participated in organized sport during their youth followed by a lapse in PA, often due to competing responsibilities (Dionigi, 2015). Uniquely, ‘Rekindlers’ have overcome prolonged disengagement from PA in the past. The purpose of this study was to understand the facilitators, barriers, and strategies of ‘Rekindlers’ during their re-engagement following a sustained lapse in PA. A multiple-case study grounded in critical realism was conducted with eleven MAs (9F, 2M), ranging from 43-70 years of age. Data was collected through semi-structured interviews and data was analyzed through thematic analysis. All MAs were competitive in youth sport as children and adolescents and experienced a lapse in sport (ranging from 3-40 years) due to varying factors (e.g., career, injury). MAs spoke about reasons for re-engaging (e.g., health, identity, social involvement) and the research they did to re-engage (e.g., club websites, spoke with established MAs). MAs’ re-engagement process was influenced by their personal strategies (e.g., preparatory PA, goal setting), and social (e.g., support from MAs/coaches), external (e.g., proximity to training), and psychological factors (e.g., identity, emotions). These results offer an example of successful re-engagement and may be helpful for others attempting to re-engage following the COVID-19 pandemic.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".