Barriers and Facilitators to Advance Care Planning Implementation for Patients with Neurodegenerative Diseases among Indian Physicians: A Mixed-Methods Analysis
Bibliographic record
Abstract
BACKGROUND: Advance care planning (ACP) is a process that enables individuals to define and communicate their goals and preferences for future medical care, especially in chronic, progressive illnesses such as Parkinson's disease (PD) and other neurodegenerative disorders. Despite its recognized benefits in improving patient autonomy and end-of-life care outcomes, ACP remains underutilized in India. This study aimed to assess the attitudes and practices of Indian neurologists and geriatricians regarding ACP, identify perceived barriers, and suggest strategies to improve uptake. METHODS: A mixed-methods approach was employed in this study. In the first phase, a structured online survey was distributed to physicians across India who cared for patients with PD and neurodegenerative disorders. The survey collected demographic information and ACP-related practices, attitudes, and perceived barriers. In the second phase, in-depth qualitative interviews were conducted with a purposively sampled subset of respondents, and inductive thematic analysis was performed to gain deeper insights. RESULTS: A total of 140 physicians participated in this survey. Although 93.6% acknowledged the necessity of ACP, only 25% felt they had sufficient time, and 20% felt they had adequate resources for meaningful discussions. Lack of legal clarity (52.1%), training (16.4%), and institutional support (65.7%) were commonly cited as barriers. Qualitative interviews with 15 respondents revealed additional challenges, such as concerns about provoking hopelessness and denial from patients and families. The interviews also revealed that physicians confused ACP with advanced treatment strategies or treatment of advanced disease. Nevertheless, some physicians shared positive experiences, noting that early personalized discussions improved trust and communication, which could facilitate uptake of ACP. CONCLUSIONS: We identified several systemic, professional, and physician-perceived sociocultural barriers that hinder ACP implementation. To bridge this gap, legal reforms, structured ACP training, and public awareness initiatives are necessary. Tailored culturally sensitive models involving multidisciplinary teams may improve ACP adoption within the Indian context. Future research could explore whether alternative terminology, such as 'future care planning', may improve clarity and acceptance in this context and avoid confusion with advanced therapies or treatment of advanced disease.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".