How Canadian High-Performance Coaches Adopt and Implement Technology: Exploring the Antecedents of Intra- and Interorganizational Trust, Technological Proficiency, and Subjective Norms and Social Influence on Technology Adoption
Bibliographic record
Abstract
Technological innovation has been shown to make meaningful impacts across several aspects of high-performance sport. Although the literature on technology adoption and implementation is vast, currently very little exists on the coach’s perspective in this regard. This investigation explored Canadian high-performance sport coaches and their relationship with technology adoption. Eleven coaches from both summer and winter National Sport Organizations were interviewed using a semistructured format. Three thematic categories were identified. First, intra- and interorganizational trust showed that coaches valued being aware and informed of any planned changes, and that educating them on technology adoption was important for gaining their participation. Second, coaches mentioned that they took the time to search for and build a fully functional technology, and valued collaboration among their peers for sharing technology-based knowledge. Finally, many of these high-performance coaches also depended upon their relationships to mentor coaches or other coaches in the field to identify and apply new technology. The outcome of this research may provide insight into shaping future policies to help sport coaches get access to the technology that more directly meets their needs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".