Hispanic Patient Perspectives of the Physician’s Role in Obesity Management
Bibliographic record
Abstract
BACKGROUND: Little is known concerning Hispanic patients' perceptions about the role of the physician in obesity management. This study seeks to describe the perspectives of Hispanic patients toward weight loss, and what they believe their doctor's role should be in the management of obesity. METHODS: A cross-sectional study utilizing semi-structured interviews was conducted in a university-based family medicine clinic. Open-ended questions explored beliefs about the relationship between weight and health, previous weight loss experience, perceptions about the role of the physician in weight loss, past experiences with their physician, and preferences for how a physician could help facilitate weight loss. The free recall listing technique was used to elicit responses. Common themes were identified by a group coding process. RESULTS: Patients were open to discussion from physicians concerning weight loss but many had not been approached. They wanted assistance from their doctors in the form of dietician referrals, specific weight loss goals, and encouragement. Patients' knowledge about the implications of excess weight on health was lacking. CONCLUSION: Hispanic patients want more help and advice from their doctors. General knowledge of the health implications of obesity was lacking, indicating a need for more health education by the healthcare team.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".