Patient Preferences for Attributes of Health Canada Approved Weight Loss Medications Among Adults Living with Obesity in Canada: A Qualitative Study
Bibliographic record
Abstract
Purpose: Obesity is a complex disease with negative impacts on physical and mental health. The treatment of obesity is an area where shared decision making and patient preferences play an important role. Recommendations surrounding weight loss medications are evolving and only recently, with the publication of the 2020 Canadian Obesity Management Clinical Guidelines, pharmacotherapy has become a recommended alternative for obesity management. Guidelines recommend three medications: orlistat, liraglutide, and naltrexone/bupropion. This study sought to identify medication attributes relevant to patients starting pharmacotherapy for weight management. Patients and Methods: Semi-structured focus groups and interviews were conducted with Canadian residents who were ≥ 18 years of age and were living with obesity (body mass index [BMI] ≥ 30kg/m 2 or ≥ 27kg/m 2 with adiposity-related complications). Sessions were conducted virtually, audio recorded, and transcribed. Two team members used a combination of inductive and deductive coding to independently code the data. A final coding template was agreed upon through discussion. Results: A total of 21 individuals participated (85.7% female, 76.2% ≥ 40 years of age) with the average BMI being 44.3 kg/m 2 . Participants touched upon many attributes which were categorized into five categories: 1) cost, 2) regimen, 3) side effects, 4) benefits, and 5) non-medication attributes. Cost of medications, lack of coverage by insurance companies, and stigma were identified as major barriers to accessing medications. There was consensus in the desire for a simple regimen, however there was heterogeneity among opinions on tolerability of side effects, desired benefits, and route of administration. Conclusion: This study identified attributes that influenced patient’s decisions when considering a new anti-obesity medication. Understanding these attributes can assist clinicians in shared decision-making. This study highlighted the stigma that is prevalent among providers and the need for education. Further research should be conducted to understand the tradeoffs patients in our study make between the identified attributes. Keywords: patient preferences, obesity, weight loss, anti-obesity medications, Canada
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".