Healthcare Providers’ Experiences as Arts-Based Research Participants: “I Created My Story About Disability and Difference, Now What?”
Bibliographic record
Abstract
Little is known about the experiences of healthcare providers as research participants in qualitative studies employing methods that encourage disclosure of their own disabilities. In this paper, we describe the experiences and implications of creating personal stories of disability and difference for healthcare provider participants in an arts-based study. The study design is a supplementary secondary analysis of a subset of data from a larger study focused on transforming negative concepts of disability and difference entitled, Mobilizing New Meanings of Disability and Difference: Using Arts-Based Approaches to Advance Healthcare Inclusion for Women with Disabilities. This supplementary study explores the experiences and perspectives of 17 healthcare provider participants who completed semi-structured interviews following creation of a multi-media story about their experience of disability or difference. Using creative non-fiction methods, two narrative streams are identified about healthcare provider experiences and the impacts of participating. The first addresses shared positive experiences about the research. The second entails more ambivalent reflections on their involvement as participants. The tension between the two experiences generates considerations to forward a mutually beneficial alliance to disrupt ableist understandings in healthcare and reveals new meanings of disability that are agential and integral to the stories and storytellers themselves.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".