Advocacy through storytelling: challenging eating disorders and eating disorders stigma
Bibliographic record
Abstract
BACKGROUND: Although eating disorders (EDs) are among the most stigmatised mental illnesses, a number of individuals break past this stigma and engage in ED advocacy by sharing their recovery stories. Little is known, however, about the role of such advocacy in their healing journeys. METHODS: To bridge this gap, the authors examined the role of autobiographical oral storytelling in the ED recovery of adult advocates. Autobiographical oral history interviews were carried out with adult advocates (n = 16) recovering from EDs. The data were analysed using a mixture of actantial and thematic analyses. Authors also used activity theory to categorise how storytelling was translated into concrete social actions. Results were then interpreted through frameworks of embodiment and the intersectionality of identity. RESULTS: Advocates chose to share their ED stories as a way to embody resilience and make meaning from their ED experiences. Beyond personal gains, the social benefits of sharing their stories included raising hope and openness to converse further with audiences, advocating for greater ED resources (e.g., ED literacy among school staff), and offering new training initiatives for healthcare professionals. The ties between storytelling and the unique aspects of one's identity are also discussed. CONCLUSIONS: Engaging in advocacy through storytelling can positively affect both the advocates and the audiences with whom they connect. Future studies, informed by feminist biopsychosocial frameworks, can examine storytelling as a therapeutic intervention. Such frameworks serve as alternatives to biomedical models of EDs and mental illnesses. They also emphasise the need for broader changes that destabilise oppressive body cultures and display how storytelling can help mobilise change.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".