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Record W6941122591 · doi:10.11575/prism/46475

Examining Weight Bias among Practicing Canadian Family Physicians

2019· other· en· W6941122591 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2019
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsBlameObesityFeelingPublic healthHealth carePerceptionHealth professionalsWeight loss

Abstract

fetched live from OpenAlex

Objectives: The aim of this study was to examine the attitudes of practicing Canadian family physicians about individuals with obesity, their healthcare treatment, and perceptions of obesity treatment in the public healthcare system. Method: A national sample of Canadian practicing family physicians (n = 400) completed the survey. Participants completed measures of explicit weight bias, attitudes towards treating patients with obesity, and perceptions that people with obesity increase demand on the public healthcare system. Results: Responses consistent with weight bias were not observed overall but were demonstrated in a sizeable minority of respondents. Many physicians also reported feeling frustrated with patients with obesity and agreed that people with obesity increase demand on the public healthcare system. Male physicians had more negative attitudes than females. More negative attitudes towards treating patients with obesity were associated with greater perceptions of them as a public health demand. Conclusion: Results suggest that negative attitudes towards patients with obesity exist among some family physicians in Canada. It remains to be determined if physicians develop weight bias partly because they blame individuals for their obesity and its increased demand on the Canadian public healthcare system. More research is needed to better understand causes and consequences of weight bias among health professionals and make efforts towards its reduction in healthcare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.019
GPT teacher head0.171
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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