Psychosocial factors predicting the usage of technology by golfers
Bibliographic record
Abstract
Technology has become an important resource in sport that can help athletes improve their performance. However, the factors that predict the use of technology among athletes are unknown. In an effort to understand the current use of technology, we examined factors that impact technology use in sport. Human technology research in other domains suggests that an individual's trust in technology may be an important predictor of whether they use technology. Specific to sport, an athlete's use of a coach, self-regulated learning, skill level, playing experience, and gender may also influence their technology use. Therefore, the purpose of the present study was to determine which factors predict golfers’ use of technology and, through a secondary analysis, to explore how predictive factors differed between athletes who used technology and/or a coach. A one-time survey that gathered demographic and golfing-specific (Skill Level, years of playing Experience) information, and measured technology use, coach use, trust in technology, and self-regulated learning was completed by 313 golfers. Logistic regression determined that golfers’ use of a coach, trust in technology, self-regulated learning, and skill level predicted their use of technology. Further, a two-way factorial analysis of variance demonstrated that there were differences in trust in technology, self-regulated learning, and skill level between golfers who did and did not use technology. The findings of this novel study create a foundation for future research in this area and are the first step in determining how athletes can best use technology in their training and competition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".