Barriers and facilitators to diagnosing and managing apathy in Parkinson’s disease: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Apathy is a prominent non-motor symptom in Parkinson's disease (PD). People with apathy show a lack of emotion, passion, and motivation. Between 17 and 70% of persons with PD have apathy; the extreme heterogeneity in these estimates is due to limited heterogeneous knowledge concerning how to diagnose PD. The lack of a widely utilized diagnostic process limits understandings on how to treat and manage apathy in PD. A scoping review of apathy in PD identified only one qualitative study investigating this symptom. It was our objective to assess perceived barriers and facilitators to diagnosing, treating, and managing apathy in PD, as described by key stakeholders. METHODS: This research applied qualitative methodology, utilizing focus groups and interviews with health care practitioners (HCPs), persons with PD, and caregivers. Evidence gathered from a scoping review on apathy in PD informed discussions that took place with participants. Data collection and analysis was conducted using framework analysis, applying the Theoretical Domains Framework and Behaviour Change Wheel. RESULTS: Eleven HCPs and five persons with PD/caregivers participated. Themes included interdisciplinary teams and communication with family to facilitate diagnosis and treatment, and the use of education and increased awareness of apathy to facilitate management. Themes surrounding barriers included lack of initiative and motivation to maintain treatment plans, and a lack of evidence for apathy specific interventions. While a key barrier identified was the lack of information HCPs have access to, persons with PD and caregivers would prefer to receive a diagnosis of apathy even with limited management methods. Thus, education and awareness were noted as two of the most important facilitators, overall. CONCLUSION: These findings suggest that diagnosing, treating, and managing apathy in PD requires interdisciplinary teams, that include family and caregivers. We identified that where HCPs perceive lack of knowledge as a barrier to diagnosis, persons with PD and caregivers find being given a diagnosis facilitates understanding. These findings highlight the importance of qualitative research involving persons with PD and apathy, caregivers, and HCPs who aid in management of this symptom. Barriers reported suggest future research must aim to identify apathy specific treatments, both pharmacologic and non-pharmacologic.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".