The Self-Regulation and Smartphone Usage Model: A Framework to Help Athletes Manage Smartphone Usage
Bibliographic record
Abstract
Self-regulation is essential for optimal development, performance, and well-being in sport, and smartphones may support and hinder this self-regulation. The relationship between smartphones and self-regulation has seldom been investigated in sport. Thus, the purpose of this study was to examine self-regulatory processes, conditions, and outcomes related to athletes’ smartphone usage. Twenty-four competitive and high-performance athletes from eight sports participated in individual interviews informed by the models of self-regulated learning and self-regulatory strength. Themes created from a directed content analysis aligned with components of both models and were integrated with new themes to form the “Self-regulation and Smartphone Usage Model” (SSUM). The SSUM illustrates a cyclical model of self-regulation and smartphone usage across five components: self-regulation capacity, processes, conditions, outcomes, and competencies. While self-regulation demands can be increased because of smartphones and lead to depletion, smartphones can be powerful vehicles to strengthen self-regulation competencies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".