Bibliographic record
Abstract
In many wealthy countries, fatness is stigmatized as a sign of personal failure. Health care interactions can enact fat-related stigmatization, which can worsen health outcomes. The present analysis highlights how stigmatizing discourses about fat bodies emerge in primary care appointments, and examines immediate conversational effects. METHODS: Observational study in three primary care clinics in Canada, using conversation and discourse analytic methods on transcripts of 29 audio-recorded appointments with adults. Talk about weight and blood pressure are contrasted. RESULTS: During measurement and review of measurements, clinicians routinely interpreted the blood pressure result but rarely interpreted weight. Patients of varied ages and body sizes often filled the interpretative vacuum, and focused on behaviors. Overall, neither patients nor clinicians challenged the stigmatizing discourses associated with fat bodies, but sometimes agreed that the "personal failure" frame associated with fatness does not apply to the particular patient. Physicians rarely raised other determinants of weight, but often did so when talking about blood pressure. CONCLUSIONS: Across most body types and ages, weight-related talk spurred stigma management from adult patients. Patients' interpretations were consistent with accepting or avoidant strategies to manage stigma. The findings challenge clinicians and researchers to frame patients' defensiveness or sensitivity as a predictable response to mitigate stigma, and consider how clinical care might be better structured to avoid stigmatization. Recognizing the range of determinants of weight with interpretation of weight may help, particularly if combined with other methods to de-stigmatize care. The results have implications for clinical weight management and behavior change support.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".