The influence of coaching efficacy on trust and usage of technology in golf instruction
Bibliographic record
Abstract
The rise of technology in sport has provided coaches with another tool to aid athlete development, but there is little research on its relationship to coaching practices. Research in non-sport domains has demonstrated a relationship between user trust in and use of technology. The user's confidence can also affect this relationship, where higher confidence is typically associated with less technology use. Minimal work has examined factors that influence technology use within the sports domain; therefore, the present study sought to determine whether coaching experience and coaching efficacy could predict golf coaches' use of technology in training. A one-time survey that gathered demographic information, and measured coaching experience, coaching technique efficacy, trust in technology and use of technology was completed by 83 registered Professional Golfers Association golf coaches and instructors. Results showed that coaching technique efficacy was predictive of coaches’ use of technology in training, where more technique efficacy resulted in increased use of technology. Mediation analyses showed that this association was mediated by their levels of trust in technology. There was no relationship between coaching experience and use of technology. Therefore, coaching technique efficacy, rather than experience, seems to be an important variable in predicting coaches' use of technology in training and instruction. Further, because higher efficacy predicted increased usage, the results illustrate the differences between the sport training environment and other non-sport domains regarding the factors that influence technology use. These findings are an important first step towards investigating how technology can be used by coaches to best improve athlete performance.
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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 | 0.003 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".