Assessment and Conceptualization of Perceived Competence in Ice Hockey: A Scale Development and Validation Study
Bibliographic record
Abstract
Perceived competence plays a crucial role in establishing environments favorable to individual development in youth sport. As most assessment tools of perceived competence are not sport-specific, they have limited usefulness for sport practitioners and researchers. This study had two-fold aims: (i) to develop a perceived competence assessment tool specific to ice hockey; and (ii) to examine its factorial structure and internal consistency. We first developed an initial 29-item version of this ice hockey competence self-report scale, using a group of ice hockey stakeholders and sports science experts to develop the items and a pilot sample of 42 hockey players to confirm test-retest reliability. Finally, we validated the scale among a cohort of 770 adolescent ice hockey players ( M age = 14.78, SD = 1.60 years). Results from exploratory factor analysis (EFA) revealed that perceived competence in ice hockey was defined by six dimensions, with rejection of seven items. Confirmatory factor analysis (CFA) suggested that the 6-factor first order model was the best fit with the conceptualization of perceived competence in ice hockey (CFI = 0.938, RMSEA = 0.044). The final, 22-item questionnaire now provides a reliable and valid measure of perceived competence in hockey for adolescent participants. It has promise for evaluating future interventions aiming to foster young athletes’ perceived confidence through sport.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".