Development and Validation of the Body Knowledge Questionnaire (Phase 2)
Bibliographic record
Abstract
This study evaluated the psychometric properties of the Body Knowledge Questionnaire (BKQ), an instrument that measures weight management integration: an individual’s attitudes, preferences, and behaviors associated with weight self-management. The BKQ was revised following a pilot study demonstrating its validity and reliability, and new items were added based on data gathered through four focus groups of obese and normal-weight survey completers. Additional items were derived from the extant literature on weight management and integration. A panel of 30 health professionals who work in the area of weight management, bariatrics, and nutrition science reviewed the revised BKQ for content validity. Two hundred sixty-seven participants, recruited through Walden University’s online participant pool, completed the revised 66-item BKQ through SurveyMonkey. Exploratory factor analysis yielded a five-factor solution (Emotional Eating, Health-Conscious Lifestyle, Conscientious Eating Habits, Food Centricity, and Psychosomatic Awareness), with factor loadings >.40. Discriminant function analysis determined that the BKQ full scale and subscales could predict the classification of participants into normal-weight and obese groups for the total sample with 71% and 79% accuracy, respectively. Test–retest reliability was .86, and internal consistency of the overall BKQ was .92. The BKQ instrument has potential for use in individual or group weight management programs and program evaluation; for use in weight management practice areas such as dietetics, diabetes education, nursing, and psychology; or in the development of new weight management interventions.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".