“Constantly justifying my existence”: Lower-income, higher-weight Canadian adults’ stigma coping mechanisms
Bibliographic record
Abstract
Individuals who are higher-weight and low-income may disproportionately experience weight and income stigmas in healthcare experiences compared to lower-weight, higher-income individuals. The ways that weight and income stigmas interact in healthcare should be better understood in order to provide better, less stigmatizing care to higher-weight, low-income patients. This study assesses how patients manage stigmatizing experiences in both healthcare and everyday experiences and how that impacts health seeking and stigma management behaviors through semistructured interviews with 11 higher-weight (Body Mass Index ≥30), low-income adults (≥18 years of age) in an Atlantic Canadian province. Participants took part in two interviews that focused on healthcare experiences and both positive and negative places/spaces. The two face-to-face interviews for each participant (total 21 interviews) were audio-recorded and professionally transcribed verbatim. The transcripts were analyzed using thematic analysis to identify recurring concepts and patterns within the data. Two major themes emerged from the data, uptake of stigmatizing, neoliberal health messaging and coping with stigma. Coping with stigma included subthemes control over stigmatizing experiences and stoicism in the face of stigma. The findings suggest that individuals understand their health and wellness through a neoliberal lens and that they deploy strategies of control and stoicism to cope with the stigmas they face.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".