Toward New Understandings of a Good Life for People Experiencing Impairment: An Art- and Community-Based Research Protocol
Bibliographic record
Abstract
Almost half of all Canadians live with sensory, cognitive, or physical impairment due to injury, illness, or ageing. Yet, research investigating the complex relationship between well-being and impairment is limited. Existing research has largely rendered invisible peoples’ experiences of a good life while living with impairment. In response, researchers are calling for interdisciplinary research that integrates new paradigms. Using innovative qualitative and visual research methods, our team will work with 24 women living with impairment as a result of cancer and/or its treatments to explore how creative practices can contribute to new understandings of a good life. Participants will be invited to complete mixed-media arts workshops during which they will create artwork representing their experiences of well-being while living with impairment. After the workshops, each participant will be asked to complete an individual interview about their experiences and their creative work. Next, a rough cut of a film will be produced from the workshop recordings and participants will be invited to provide feedback at a private screening. The research will culminate in a public screening of the film and an exhibit of participants’ work. Audience members (health professionals, students, participants, and the general public) will participate in a discussion following the screening and will be asked to complete a short survey about the event. This research will enhance public understandings of what it means to live well with impairment. In addition, the research will potentially impact health practices through querying dominant biomedical paradigms focused on curative approaches and distress.
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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 | 0.070 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.055 | 0.018 |
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".