Obesity management from the perspectives of people living with obesity in Canada: A mixed‐methods study
Bibliographic record
Abstract
Abstract Aims To identify and better understand themes related to why people living with obesity (PwO) in Canada may not use professional support and to explore potential strategies to address the challenges. Methods One‐on‐one interviews and online surveys, informed by the Theoretical Domains Framework, were conducted. A total of 20 PwO were interviewed and a separate group of 200 PwO were surveyed. Results from the interviews guided the development of the survey. Spearman's correlation analysis was performed to investigate the association between the theme domain scores of the PwO and their prior experience with obesity management strategies. Results The 200 PwO surveyed provided representation across Canada and were diverse in age, background and gender. The most prominent domains associated with use of professional support by PwO were: Intention ( r s = −0.25; p < 0.01); Social/Professional Role and Identity ( r s = −0.15; p < 0.05); and Optimism ( r s = −0.15; p < 0.05). For example, PwO without professional support less often reported being transparent in obesity discussions, perceived obesity to be part of their identity, and expected to manage the illness long term. Many PwO hesitated to use various adjunctive therapies due to concerns about affordability, long‐term effectiveness, and side effects. Conclusion This study identified contextual, perception and resource considerations that contribute to healthcare decision‐making and the use by PwO of professional support to manage obesity, and highlighted key areas to target with interventions to facilitate obesity management. Strategies such as consistent access to healthcare support and educational resources, as well as improved financial support may help PwO to feel more comfortable with exploring new strategies and take control of their healthcare.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".