Weighty words: exploring terminology about weight among samples of physicians, obesity specialists, and the general public
Bibliographic record
Abstract
BACKGROUND: The words used to refer to weight and individuals with large bodies can be used to reinforce weight stigma. Given that most previous research has examined preferred terminology within homogenous groups, this research sought to examine terminology preferences across populations. METHODS: This paper reports on data gathered with the general public, family physicians, and obesity researchers/practitioners. Participants were asked about the words they commonly: (1) used to refer to people with large bodies (general public); (2) heard in their professional contexts (physicians and obesity specialists); and (3) perceived to be the most socially or professionally acceptable (all samples). RESULTS: Similarities and differences were evident between samples, especially related to weight-related clinical terms, the word fat, and behavioral stereotypes. CONCLUSION: The results provide some clarity into the differences between populations and highlight the need to incorporate use of strategies that may move beyond person-first language to humanize research and clinical practice with people with large bodies.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".