Understanding Parkinson’s through visual narratives: “I’m not Mrs. Parkinson’s”
Bibliographic record
Abstract
Introduction Although it is accepted that individuals with Parkinson’s disease must navigate challenges such as receiving their diagnosis and changing daily occupations, little is known about how they navigate. The purpose of this study is to deepen the current understanding of the experience of living with Parkinson’s disease and its implications for occupation through a narrative visual methodology (photo-elicitation). Method Six individuals with Parkinson’s disease were asked to take photographs and share verbal narrative accounts to illustrate their experience of living with Parkinson’s disease. Findings Results highlight the interrelationship between occupation and identity, as many of the participants’ stories were interpreted as foregrounding the negotiation of occupation, and how such negotiation shaped their sense of identity. Overall, three major themes were identified: (1) Framing the meaning of Parkinson’s disease (accepting the disease as part of who they were); (2) Negotiating engagement in occupation (ongoing deliberation over whether to continue engaging in certain aspects of life as Parkinson’s disease progressed); and (3) Being ready to accept changes that impact personal or social identity (readiness to accept help and to identify as someone with Parkinson’s disease). Conclusion Attending to insights regarding the lived experience of Parkinson’s disease will enhance quality of care through informing an enriched client-centered, occupation-based approach.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".