The i-ACT™ in Obesity educational intervention: a pilot study on improving Canadian family physician care in obesity medicine
Bibliographic record
Abstract
BACKGROUND: Obesity is a chronic problem in Canada and although the Canadian Medical Association recognizes obesity as a disease, health care professionals (HCPs) are not necessarily proactively managing it as one. This study aimed to assess current obesity management knowledge and practices of Canadian family physicians (FPs) and evaluate the feasibility of an online self-directed learning platform, i-ACT™ in Obesity, in delivering learning and changing practice intentions to advance obesity management. METHODS: i-ACT™ in Obesity is an online self-directed learning program designed by Canadian obesity medicine experts to provide individualized learning curricula to participants. One hundred FPs, with an interest in weight management and managing patients with obesity, were recruited across Canada to participate in a pilot study. FP education was delivered in a stepwise manner. Each participant completed a practice profile assessment to determine knowledge gaps and educational needs. Learners then watched didactic videos across disciplines on topics assigned to their curriculum by the program algorithm based on the relative difference between indicated and desired current knowledge. FPs also completed 10 retrospective patient assessments to assess clinical management practices and planned behaviour change. Feasibility, acceptability, and satisfaction of the learning program were assessed to formulate the rationale for a more widespread deployment in the future. Survey responses and related data were analyzed using comparative measures and descriptive statistics. RESULTS: The program was piloted by ninety-one Canadian FPs, where 900 patients were assessed. FPs showed distinct differences between their current and desired levels of comfort in a variety of obesity-related topics. Participation was associated with an intention to use more obesity treatment interventions moving forward. The program received an overall satisfaction rating of 8.6 out of 10 and 100% of the evaluators indicated that they would recommend it to their colleagues. CONCLUSION: The program was overall well received and successfully changed obesity management intentions among participating FPs, thus setting the stage for a larger more comprehensive study to examine the efficacy of i-ACT™ in Obesity in addressing knowledge gaps and advancing evidence-based, guidelines-aligned approach to obesity treatment.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".