Relational developmental systems metatheory: a conceptual framework to understand and promote older adults’ involvement in sport
Bibliographic record
Abstract
Sport is viewed as a vehicle to develop or augment adaptive developmental processes, resources, and experiences across the lifespan. However, research has acknowledged that sport participation is associated with costs as well as benefits in older adulthood. To fully understand the influence of sport participation on psychosocial and developmental outcomes in older people, insight into the dynamic and complex relationship between the individual and his/her environment is needed. This review proposes Relational Developmental Systems (RDS) metatheory as a conceptual framework to understand the outcomes of sport participation later in life. Knowledge of the mechanisms, processes, and bi-directional exchanges related to individual and contextual relations emphasised in RDS frameworks may help researchers gain an understanding of the means by which sport participation shapes developmental outcomes evident within and among older athletes. Key assumptions of the metatheory are introduced and discussed in relation to the sport setting. Specific examples from the literature on older athletes are presented to illustrate the relationship between individual and contextual factors on developmental outcomes. Finally, considerations for future research on the topic are proposed using an RDS lens to move the field forward.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".