Moving beyond eat less, move more using willpower: Reframing obesity as a chronic disease impact of the 2020 Canadian obesity guidelines reframed narrative on perceptions of self and the patient–provider relationship
Bibliographic record
Abstract
Obesity is becoming recognized as a complex, chronic medical condition. However, the dominant treatment narrative remains that goal weight can be achieved by eating less, moving more using willpower, placing responsibility for change on the person with obesity (PwO). This study evaluated the impact of revising this narrative, to viewing obesity as a treatable medical condition, on internalized weight bias and perceived patient-provider relationship. PwO were recruited into an online study in which two videos were presented; the first showing a traditional doctor endorsing the eat less, move more approach, and the second showing a doctor describing obesity as a treatable medical condition. After each video participants were asked to imagine that they were being treated by that doctor and completed the Weight Bias Internalization Scale (WBIS) and the Patient-Health Care Provider Communication Scale (PHCPCS). A total of 61 PwO (52% response rate) completed the protocol. Compared to the traditional narrative video, the revised narrative video resulted in significant reductions in WBIS scores and significant increases in the PHCPCS scores and was preferred by participants. Within the context of this small-scale study evidence supports that the revised narrative promoting obesity as a complex, chronic but treatable medical condition that is not the result of personal failure has a positive impact on the perceived patient-provider relationship and is associated with reductions in internalized weight bias. This evidence supports the need to help PwO reframe obesity from a personal failure to a legitimate medical condition worthy of care.
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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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".