Development and Evaluation of the High-Intensity Interval Training Self-Efficacy Questionnaire
Bibliographic record
Abstract
This study involved the design and evaluation of the High-Intensity Interval Training Self-Efficacy Questionnaire (HIIT-SQ). Phase 1: Questionnaire items were developed. Phase 2: Australian adolescents (N = 389, 16.0 ± 0.4 years, 41.10% female) completed the HIIT-SQ, and factorial validity of the measurement model was explored. Phase 3: Adolescents (N = 100, age 12-14 years, 44% female) completed the HIIT-SQ twice (1 week apart) to evaluate test-retest reliability. Confirmatory factor analysis of the final six items (mean = 3.43-6.73, SD = 0.99-25.30) revealed adequate fit, χ2(21) = 21, p = .01, comparative fit index = .99, Tucker-Lewis index = .99, root mean square of approximation = .07, 90% confidence interval [.04, .11]. Factor loading estimates showed that all items were highly related to the factor (estimates range: 0.81-0.90). Intraclass coefficients and typical error values were .99 (95% confidence interval [.99, 1.00]) and .22, respectively. This study provides preliminary evidence for the validity and reliability of scores derived from the HIIT-SQ in adolescents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".