Environmental Scan of Weight Bias Exposure in Primary Health Care Training Programs
Bibliographic record
Abstract
Negative attitudes and beliefs about individuals with obesity (also known as weight bias) have negative consequences for physical and mental health for individuals with obesity and impact the quality of care provided by health professionals. A preliminary environmental scan of college and university training programs was conducted consisting of 67 degree and diploma granting programs from 22 institutions in Alberta, targeting programs training future health professionals. Publicly available online course descriptions were examined for weight-related keywords. Keyword frequency was used to determine the extent that coursework addressed weight-related issues. The results suggested that courses are structured to include learning about general health promotion as well as lifestyle factors that may contribute to obesity but may not systematically include learning about weight bias or its potential impact. Our findings highlight the need for further in-depth investigations as well as the need to enhance current curricula in higher education by including information related to weight, obesity and weight bias. Les attitudes et les croyances négatives concernant les personnes obèses (également connues comme partialité contre les obèses) ont des conséquences négatives sur la santé physique et mentale des personnes obèses et affectent la qualité des soins qui leur sont prodigués par les professionnels de la santé. Nous avons mené une étude environnementale préliminaire des programmes de formation universitaires et collégiaux qui a porté sur 67 programmes menant à un certificat ou à un diplôme dans 22 établissements d’Alberta, et nous avons principalement visé les programmes de formation de futurs professionnels de la santé. Les descriptions de cours en ligne accessibles au grand public ont été examinées et les mots clés faisant référence aux problèmes de poids ont été identifiés. Les résultats suggèrent que les cours sont structurés de manière à inclure l’apprentissage de la promotion de la santé en général ainsi que les facteurs relatifs au style de vie qui peuvent contribuer à l’obésité mais ils n’incluent pas l’apprentissage systématique de la partialité contre les obèses ou ses effets potentiels. Nos résultats illustrent le besoin de mener des enquêtes approfondies ainsi que celui de renforcer les programmes de cours actuels en enseignement supérieur pour y inclure des informations relatives au poids, à l’obésité et à la partialité contre les obèses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".