Addressing Internalized Weight Bias and Changing Damaged Social Identities for People Living With Obesity
Bibliographic record
Abstract
Obesity is a stigmatized condition due to pervasive personal, professional, institutional and cultural weight bias. Individuals with obesity experience weight bias across their lifespan and settings, which can affect their life chances and significantly impact health and social outcomes. The objectives of this study were to: a) explore weight bias and stigma experiences of people living with obesity; b) develop counterstories that can reduce weight bias and stigma; and c) reflect on current obesity master narratives and identify opportunities for personal, professional and social change. Methods: Using purposive sampling, we lived alongside and engaged persons with obesity (n=10) in a narrative inquiry on weight bias and obesity stigma. We co-developed interim narrative accounts while applying the three-dimensional narrative inquiry space: a) temporality b) sociality; and c) place, to find meaning in participants’ experiences. We also applied the narrative repair model to co-create counterstories to resist oppressive master narratives for participants and for people living with obesity in general. Results: We present ten counterstories, which provide a window into the personal, familial, professional and social contexts in which weight bias and obesity stigma take place. Discussion: A fundamental driver of participants’ experiences with weight bias is a lack of understanding of obesity, which can lead to internalized weight bias and stigma. Weight bias internalization impacted participants’ emotional response and triggered feelings of shame, blame, vulnerability, stress, depression and even suicidal thoughts and acts. Participants’ stories revealed behavioural responses such as avoidance of health promoting behaviours and social isolation. Weight bias internalization also hindered participants’ obesity management process as well as their rehabilitation and recovery strategies. Participants embraced recovery from internalized weight bias by developing self-compassion and self-acceptance and by actively engaging in efforts to resist damaged social identities and demanding respect, dignity, and fair treatment. Conclusion: Narrative inquiry combined with the narrative repair model can be a transformative way to address internalized weight bias and to resist damaged social identifies for people living with obesity. By examining experiences, beliefs, values, practices and relationships that contribute to master obesity narratives, we can address some of the negative views of individuals with obesity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".