Identification of educational needs in the management of overweight and obesity: results of an international survey of attitudes and practice
Bibliographic record
Abstract
Despite the availability of a growing range of interventions to assist control of body weight for people with excess weight or obesity, only a small proportion of people achieve their weight loss goals and are able to maintain body weight reductions in the long term. Negative attitudes and beliefs are often found among physicians and others involved in treating obesity and may adversely impact the effectiveness of management. In this international study, healthcare professionals were invited to complete an online survey of their attitudes and practice in the management of excess body weight. A total of 335 clinicians completed the survey of whom approximately half were based in Europe. A key finding from the survey is that, while participants are generally confident in their ability to manage overweight and obesity effectively, they also report that most of their patients are not successful in achieving their weight loss goals. At the same time, participants tended to overestimate the effectiveness of current medical management in maintaining reductions in body weight. Educational initiatives addressing the real-life effectiveness of different weight control interventions may help to close the gap between clinicians' perceptions and reality in the management of excess body weight.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".