“He told me my pain was in my head”: Testimonial injustice in patient-physician relationships
Bibliographic record
Abstract
Women living with chronic pain are more likely than men to experience pain dismissal, receive nonspecific diagnostics, receive fewer follow-ups, have their condition undertreated, and be told that it results from a psychological condition. This is particularly concerning for adolescent girls living with scoliosis, who, given the progressive nature of their condition, require timely diagnosis to allow for less invasive treatment options to be explored. This population is also significantly more likely to have their condition progress to a curve angle where treatment such as bracing or spinal fusion surgery is required, both of which are associated with chronic pain. However, timely diagnosis depends on clinicians taking patients’ testimony regarding their health concerns seriously and investigating their claims. This presentation will dive into the gender gap in care for adolescent girls living with chronic pain caused by scoliosis, focusing on their experiences of pain dismissal and its negative short and long-term effects. Leveraging the concept of intersectionality, the authors argue that adolescent girls may suffer a testimonial injustice when their pain is dismissed by clinicians. This presentation will also explore gender-specific peer support groups as a possible mitigating factor to testimonial injustice and other negative outcomes from chronic pain and pain dismissal. The researchers interviewed members from scoliosis peer support group Curvy Girls using open-ended questions, gathering narrative data about their experiences that was subsequently analyzed using an applied philosophical hermeneutics approach, along with intersectionality and testimonial injustice as part of their framework.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".