Attitudes and behaviours of Canadian National Team coaches regarding the use of technology in their coaching practice
Bibliographic record
Abstract
Previous literature has proposed that successful adoption measures involve positive attitudes regarding using technology, high levels of technological self-efficacy beliefs, low levels of perceived complexity, and high levels of perceived relative advantage.The purpose of this study is to investigate these antecedents of technology adoption among national team Canadian coaches.Twenty-five current and retired Summer and Winter Canadian national team coaches participated in this investigation.They completed four questionnaires: a general information form, the Affinity for Technology Interaction Scale (ATI), the Computer Self-Efficacy Measure (CSEM), and the Perceived Relative Advantage and Perceived Complexity Scales (PRA/PCo).Canadian national team coaches who responded were found to have a moderate affinity for technology when engaging with technology in their coaching practice.They also reported to have very high self-efficacy when it comes to using technology.They were shown to have a moderately high conviction in their ability to use technology.Coaches also viewed technology as giving them a high relative advantage over not using technology.Finally, they generally viewed technology as not very complex to operate.Most Canadian national team coaches who responded showed favourable views regarding using technology, had belief in their ability, and seemed capable of overcoming challenges in using technology.Future investigations should also identify elite coaches who do not use innovations and focus on sport specific challenges in adopting or implementing technology, as well as identifying barriers coaches face when acquiring or using new technology.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".