Helping providers address psychological aspects of obesity in routine care: Development of the obesity adjustment dialogue tool (OADT)
Bibliographic record
Abstract
Background: This study developed and validated a dialogue tool (Obesity Adjustment Dialogue Tool) to efficiently assess QoL and drive to eat for use in routine clinical care. Methods: A 13-question interview was created, assessing the impact of living with obesity on quality of life and drive to eat. In a counter-balanced order, PwO were interviewed and completed the Obesity Adjustment Survey (OAS), the Impact of Obesity on Quality of Life-Lite scale (IWQoL), the Three Factor Eating Questionnaire (TREQ), and the Control of Eating Questionnaire (COEQ). Questionnaire results were used to validate the interview using correlational and concordance measures. Results: 101 PwO consented and 98 completed all measures (mean BMI = 37.8; 30.7% Class III obesity). Correlations between the QoL dialogue tool and validated instruments (OAS, IWQOL) were moderate to high. Correlations between cravings questions and validated measures (TFEQ, COEQ) were high except for attempts to control eating. Correspondence based on categorizing both the dialogue tool and scales into high/low impact was high except for attempts to control eating (which was dropped from the final tool). Conclusion: The Obesity Adjustment Dialogue Tool is a brief clinician-led structured interview which closely matches information derived from validated scales. This tool offers an efficient approach to incorporating QoL factors into obesity management.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".