“He told me my pain was in my head”: mitigating testimonial injustice through peer support
Bibliographic record
Abstract
Introduction Women with disabilities are exposed to sexism and ableism, earn less income, and work in exceptionally challenging conditions compared to women without disabilities and men with or without disabilities. Adolescent girls living with scoliosis may begin experiencing this compounding bias during their encounters with healthcare from the moment they start noticing differences in their bodies. Being significantly more likely than boys to progress to a curve angle where painful treatment such as bracing or spinal fusion surgery is required, adolescent girls living with scoliosis are therefore more likely to experience chronic pain. The long-term impact of pain and pain-related stigma includes lower educational attainments, decreased vocational functionality, and social impairments in adults after having experienced chronic pain in adolescence. Approach In this article, the authors will explore the effects and mechanisms of gender-specific peer support in disrupting this trajectory to adverse outcomes. Through individual interviews consisting of open-ended questions, the researchers gathered narrative data fromCurvy Girlsmembers, a community-based peer support group for girls and young women living with scoliosis. The data was analyzed using an applied philosophical hermeneutics approach, with intersectionality and testimonial injustice as their framework. Findings They found that the study participants had their pain narratives reinterpreted by the adults in their lives, including their parents and healthcare practitioners, leading them to question and doubt their own experiences. Discussion These negative outcomes were mitigated through the peer support they received and offered fromCurvy Girls. Participants reported having gained confidence and a sense of belonging after they joined this group, allowing them to better cope with their condition more effectively in different facets of their lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".