Reliability and validity of the Developmental History of Athletes Questionnaire (DHAQ)
Bibliographic record
Abstract
Investigations of training history profiles of highly skilled athletes and the contextual factors associated with their pathway toward expertise are essential for providing recommendations for effective sport programming. However, several limitations in the sport expertise development literature restrict the application of current knowledge to practical settings. Results and recommendations from existing studies are inconsistent, with discrepancies likely related to a combination of small sample sizes and differences in measurement tools. Furthermore, the questionnaires and interview guides utilized are generally poorly validated. To begin to address these limitations the Developmental History of Athletes Questionnaire (DHAQ) was constructed and rigorously validated. Fifteen athletes, thirteen parents, and nine coaches participated in the validation process. Athletes completed the DHAQ twice, and all athletes, parents, and coaches participated in a semi-structured interview. Responses from athlete's time 1 completion of the DHAQ were compared to a) time 2 to assess test-retest reliability, b) the athlete interview to assess concurrent validity, and c) the parent and coach interviews to assess convergent validity. Percent agreement values and intraclass correlation coefficients were utilized to develop criteria for classifying the reliability and validity of each questionnaire item, resulting in a robust instrument for the collection of athlete developmental histories.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".