Bright-siding stigma: Older adults’ experiences at a higher weight in Atlantic Canada
Bibliographic record
Abstract
The lived experiences of higher-weight people vary; homogenous samples may fail to capture this diversity. This study develops an in-depth understanding of the lived experiences of higher-weight (Body Mass Index ⩾ 30) older adults (⩾60 years of age) in a Canadian Atlantic province. Participants ( n = 11) were interviewed face-to-face using a semi-structured interview guide twice at 2-to-3-month intervals regarding their perceived treatment in social and health situations; how positive and negative healthcare experiences affected their health, lifestyles and healthcare seeking-behaviour; and recommendations in terms of patient experiences, access and inclusion. Participants infrequently reported negative experiences; however, participants’ experiences were informed by uptake of moralistic, neoliberal discourses. Thematic content analysis identified two major themes: active citizenship (participants demonstrated internalisation of the imperative for weight loss, healthy lifestyles and active ageing) and bright-siding (participants expressed that a positive attitude could prevent/help cope with stigma). Results suggest that individualistic, rather than collective, political solutions to health and stigma have been taken up by higher-weight older adults in a Canadian Atlantic province, which may hinder attempts at structural reforms addressing stigma.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".