Integrating Weight Bias Awareness and Mental Health Promotion Into Obesity Prevention Delivery: A Public Health Pilot Study
Bibliographic record
Abstract
INTRODUCTION: Promoting healthy weight is a top priority in Canada. Recent federal guidelines call for sustained, multisectoral partnerships that address childhood obesity on multiple levels. Current healthy weight messaging does not fully acknowledge the influence of social determinants of health on weight. METHODS: An interactive workshop was developed and implemented by a team of academic researchers and health promoters from the psychology and public health disciplines to raise awareness about 1) weight bias and its negative effect on health, 2) ways to balance healthy weight messaging to prevent the triggering of weight and shape preoccupation, and 3) the incorporation of mental health promotion into healthy weight messaging. We conducted a full-day workshop with 342 Ontario public health promoters and administered a survey at preintervention, postintervention, and follow-up. RESULTS: Participation in the full-day workshop led to significant decreases in antifat attitudes and the internalization of media stereotypes and to significant increases in self-efficacy to address weight bias. Participants reported that the training heightened their awareness of their own personal weight biases and the need to broaden their scope of healthy weight promotion to include mental health promotion. There was consensus that additional sessions are warranted to help translate knowledge into action. Buy-in and resource support at the organizational level was also seen as pivotal. CONCLUSION: Professional development training in the area of weight bias awareness is associated with decreases in antifat attitudes and the internalization of media stereotypes around thinness. Health promoters' healthy weight messaging was improved by learning to avoid messages that trigger weight and shape preoccupation or unhealthful eating practices among children and youth. Participants also learned ways to integrate mental health promotion and resiliency-building into daily practice.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".