Community-Based Palliative Care for Patients with Cerebral Palsy and Intellectual Developmental Disability: A Mixed-Methods Analysis of Current Practices
Bibliographic record
Abstract
Individuals with Cerebral Palsy (CP) and/or Intellectual and Developmental Disabilities (IDD) face difficulties accessing equitable palliative care. This study uses a mixed methods approach to assess the knowledge and comfort of community-based healthcare workers in providing palliative care for this patient population. Quantitative data from 54 palliative care practitioners was obtained through an online survey and qualitative analysis was obtained through a subsequent semi-structured interview with a focus group of four providers. Findings suggest providers were most confident in managing physical symptoms (66.7% dyspnea, 68.6% pressure ulcers, 72.7% restlessness) and having goals of care discussions (89.5%), but least confident in managing emotional pain (41.1%), grief (28.6%), and prognostication (34.6%). Similar themes were identified in the focus group, with an emphasis on the importance of patient autonomy and communication, as well as the challenge of facing personal and systemic biases. Importantly, both the survey and focus group echoed the need for more training that specifically addresses the needs of this population. Results suggest that challenges and discomfort in providing palliative care to patients with CP and IDD continue to exist and further training that incorporates the meaningful perspectives of these patients is needed.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".