“A Very Resilient Bunch”: A Photographic Exploration into the Experiences of Women with Turner Syndrome
Bibliographic record
Abstract
Turner Syndrome (TS) results from the total or partial loss of the second X chromosome in females. Approximately 73,469 individuals across Canada and the U.S. are affected by TS. While the amount of research on TS has grown significantly in the past few decades, this work has traditionally taken a medicalized and deficit-based approach to understanding this diagnosis. Therefore, the voices and experiences of women with TS are largely absent from the extant literature. To fill this gap in the literature, this study used an arts-based, participatory action research method, Photovoice, to explore women’s’ experiences with TS and as community members. The project was guided by the following research questions: Q1) How does a diagnosis of Turner Syndrome (TS) impact the lives of women living with TS? Q2) What role do face-to-face (FTF) and online communities for individuals affected by TS play in supporting, facilitating, or creating barriers to improving women's lives with TS? Nine women diagnosed with TS living in the US and Canada contributed to this project in two separate groups. Participants took part in an introductory workshop, focus group, and optional interview. All components of the project were conducted online. Themes and subthemes were identified from the focus group data using Reflexive Thematic Analysis (Braun & Clarke, 2019). There was a single overarching theme, overcoming challenges through strength, resilience, adaptation, and hope with four subthemes: 1) coping and resilience through creativity and self-expression; 2) the significance of community; 3) the need to take care of ourselves and find support physically and mentally; and 4) the importance of TS education and self-advocacy. This is the first study using Photovoice to investigate the experiences of women diagnosed with TS. This process allowed for an in-depth examination of the daily impact of this diagnosis for community members. Topics that previously have had little to no discussion in the extant literature such as community, resilience, and creativity were highlighted in this project. Moreover, challenges commonly faced by individuals with TS and ways to address these issues (e.g., advocacy and education) were discussed.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".