Patient Recommendations for Providers to Avoid Stigmatizing Weight in Rural-Based Women With Low Income
Bibliographic record
Abstract
PURPOSE: Weight stigma has become widespread within health care and disproportionately affects women, who are under greater appearance-based scrutiny than men. It is also well established that rural-based individuals with low incomes suffer greater health disparities compared with urban, higher-income counterparts, yet studies examining recommendations for nonstigmatizing health care among higher-weight women from low-income rural settings are lacking. This study examined the experiences and recommendations of higher-weight, low-income, rural women, with the aim of improving health care for similar populations. METHODS: In-depth, semi-structured interviews were conducted in a rural region of the Midwestern United States to explore participants' recommendations for redressing stigma within health care. All participants (n=25) self-identified as higher-weight, low-income, rural women. RESULTS: All participants experienced or were aware of weight stigma within health care. Themes identified from responses were understanding patients and their situations, offering options and supplemental information, communicating effectively, taking time, and having a positive attitude. Patient recommendations focused on correcting physician biases, rapport-building, and providing holistic care. CONCLUSIONS: The findings suggest that weight stigma is prevalent within health care provided to low-income women in rural U.S. Midwest and that there are specific communication and training approaches that may reduce the prevalence of weight stigma in health care.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".