Development and validation of a new tool to measure the facilitators, barriers and preferences to exercise in people with osteoporosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Despite the widely known benefits of exercise and physical activity, adherence rates to these activities are poor. Understanding exercise facilitators, barriers, and preferences may provide an opportunity to personalize exercise prescription and improve adherence. The purpose of this study was to develop the Personalized Exercise Questionnaire (PEQ) to identify these facilitators, barriers, and preferences to exercise in people with osteoporosis. METHODS: This study comprises two phases, instrument design and judgmental evidence. A panel of 42 experts was used to validate the instrument through quantitative (content validity) and qualitative (cognitive interviewing) methods. Content Validity Index (CVI) is the most commonly used method to calculate content validity quantitatively. There are two kinds of CVI: Item-CVI (I-CVI) and Scale-level CVI (S-CVI). RESULTS: Preliminary versions of this tool showed high content validity of individual items (I-CVI range: 0.50 to 1.00) and moderate to high overall content validity of the PEQ (S-CVI/UA = 0.63; S-CVI/Ave = 0.91). Through qualitative methods, items were improved until saturation was achieved. The tool consists of 6 domains and 38 questions. The 6 domains are: 1) support network; 2) access; 3) goals; 4) preferences; 5) feedback and tracking; and 6) barriers. There are 35 categorical questions and 3 open-ended items. CONCLUSIONS: Using an iterative approach, the development and evaluation of the PEQ demonstrated high item-content validity for assessing the facilitators, barriers, and preferences to exercise in people with osteoporosis. Upon further validation it is expected that this measure might be used to develop more client-centered exercise programs, and potentially improve adherence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it