Reducing weight bias and stigma in qualitative research interviews: Considerations for researchers
Bibliographic record
Abstract
Perceptions and biases influence how we interact with and experience the world, including in professional roles as researchers. Weight bias, defined as negative attitudes or perceptions towards people that have large bodies, can contribute to weight stigma and discrimination leading to negative health and social consequences. Weight bias is experienced by people living with obesity in media, health care, education, employment and social settings. In research settings, there is potential for weight bias to impact various aspects of qualitative research including the participant-researcher dynamic in interviews. However, evidence-based strategies to reduce weight bias in qualitative research interviews have yet to be identified. We discuss how weight bias may influence research interviews and identify several considerations and strategies for researchers to minimize the impact of weight bias. Strategies include practicing reflexivity, planning and conducting interviews in ways that support rapport building, using inclusive language, and considering participatory methods.
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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 | 0.739 | 0.706 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.026 | 0.052 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.011 | 0.025 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".