Determining known-group validity and test-retest reliability in the PEQ (personalized exercise questionnaire)
Bibliographic record
Abstract
BACKGROUND: To determine the known-group validity, a type of construct validity, and the test-retest reliability of a newly developed tool, the Personalized Exercise Questionnaire (PEQ), that assesses the barriers, facilitators, and preferences to exercise in individuals with low bone mass and osteoporosis. METHODS: A comparative design was used to assess known-group validity and a test-retest design to examine the reproducibility. Ninety-five participants with low bone mass and osteoporosis were recruited from an outpatient clinic in Hamilton, Ontario. The questionnaire was administered to 95 participants at baseline and a subset of 42 participants completed the survey again one week later. The known-group validity of the PEQ was determined using four hypotheses that compared two known groups based on employment level, age, socioeconomic status, and physical activity level. The reproducibility of individual responses was analyzed using the Kappa Coefficient (κ). RESULTS: There was known-group validity for three of the four hypotheses. Test-retest reliability scores ranged from no agreement to almost perfect agreement; seven items had almost perfect agreement (κ: 0.81-1.00), 12 substantial agreement (κ: 0.68-0.74), six moderate agreement (κ: 0.56-0.60), two fair agreement (κ: 0.36-0.40), one slight agreement (κ = 0.23) and one no agreement (κ = - 0.03). CONCLUSION: Preliminary support for the usefulness of the PEQ is indicated since the majority of the items had at least substantial agreement and known-group validity was moderately supported for some items. TRIAL REGISTRATION: This study was retrospectively registered with ClinicalTrials.gov , NCT03125590, on April 24, 2017.
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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 | 0.040 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".