Enhancing ice hockey parents’ emotional intelligence through a web-based parent education and support program
Bibliographic record
Abstract
Emotional intelligence (EI) is a construct that could inform parent education and support initiatives. The purpose of this study was to deliver and evaluate a web-based program developed to enhance sport parents’ EI, called the Sport Parent Emotions and Coping Support (SPECS) program. Using a sequential explanatory mixed methods approach, 29 Canadian ice hockey parents (23 mothers, 6 fathers) were randomized to either an experimental or control group with pre- and post-program measures of trait EI. Qualitative interviews were also conducted with 11 parents from the experimental group following their completion of the program. The results from a mixed ANOVA indicated a statistically significant group by time interaction with a medium-sized effect for trait EI (p = .027; ηp2 = .17) and intrapersonal EI (p = .027 ηp2 = .17). The qualitative results provided insights regarding parents’ perceptions of the program’s content and delivery and highlighted parents’ “lightbulb moments” (i.e., learning about themselves and learning about their children). The integration of quantitative and qualitative findings elucidated how aspects of the program may have contributed to changes in parents’ EI. Overall, this research yields theoretical and practical implications for youth sport stakeholders interested in delivering sport parent education and support initiatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".