A Good Life with Impairment: Illuminating and Supporting the Experiences of Women Living With Cancer through Community-Based Art Workshops
Bibliographic record
Abstract
Dominant biomedical perspectives in health care view impairment as incompatible with living a good life. This leads to care that is often focused on "fixing," falling short of supporting nuanced experiences of living a good life among people experiencing impairment. For many people, including those living with and beyond cancer, impairment is a common and "normal" part of everyday life. Scholars suggest that the arts are a powerful tool to challenge dominant perspectives and enhance understanding of complex human experiences. Therefore, this study aimed to explore and describe experiences of a good life from the perspective of women living with cancer through a series of community-based art workshops. Using an arts-based and community-based participatory research approach, we facilitated two series of virtual, community arts-based workshops. A total of 10 women participated in the study. Workshops were video-recorded, and postworkshop, individual interviews were completed. Data collection also consisted of photographs of participants' artwork. Data were analyzed using interpretive description. Our findings offer two interrelated themes that constitute key components of a good life: (a) the centrality of human connection and social relationships and (b) recognizing dynamic processes of transformation. Participants described the arts-based workshops as a valuable tool to explore, express, and support their experiences of a good life. This study provides important insights into understanding the complex experience of living a good life with cancer from personal perspectives. It also illuminates the potential of community arts in fostering positive experiences of living well with impairment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".