Enhancing Client-centeredness in Parkinson's Disease Care: Attending to the Psychosocial Implications of Lived Experience
Bibliographic record
Abstract
Evidence-based practice requires that clinicians interpret the best research evidence in the context of their clinical experience, while at the same time considering client knowledge and experiences. Although clinicians are becoming increasingly skilled at the evaluation of research evidence, the evidence-based practice process often neglects client values and self-identified health issues. Ignoring these key aspects of client-centered practice may lead to interventions that fail to target the implications of a client’s disease that are important to occupational participation and quality of life (QOL). A focus on client-centeredness is particularly important in progressive neurodegenerative disorders, such as Parkinson’s disease, where there are no known curative treatments, and interventions must instead focus on symptom management. In this paper, we explore the published literature on the psychosocial aspects of the lived experience among individuals with Parkinson’s disease, arguing that such literature provides insight into the implications of the disease and into potential treatment priorities. As such, this literature provides an additional form of evidence that raises awareness of the lived implications of this disease for clients’ occupations and QOL that, in turn, may lead clinicians to be more cognizant of client values and self-identified issues.
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 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".