Managing Safety and Risk: The Experiences of People with Parkinson’s Disease who Use Alternative and Complementary Therapies
Bibliographic record
Abstract
In this article I focus on how individuals living with Parkinson's disease manage safety and risk in their participation in alternative and complementary health care. I take a qualitative approach in this research, using semi-structured interviews and grounded theory techniques as means of generating and analysing data. My analysis centres on how these informants construct certain therapies as risk free and therefore safe, and others as risky and thus, inherently unsafe. I discuss the knowledge bases these informants draw on in their evaluations and describe the social contexts in which these assessments take place. While there is a substantial literature on risk in general, as well as the risk society, there has been less interest in micro-level analysis of risk. This article therefore contributes to knowledge through its focus on management of safety and risk in individuals' health-seeking behaviour.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".