Developing and Testing a Program to Strengthen the Dementia Palliative Care Trial Workforce
Bibliographic record
Abstract
OBJECTIVE: To describe the development and early outcomes of the National Institute on Aging (NIA)-funded Dementia Palliative Care Clinical Trials Training Program (DEM-PCCT). BACKGROUND: Nonpharmacological palliative care interventions can improve the lives of people living with dementia and their care partners, yet evidence remains limited. We developed DEM-PCCT to train investigators and enhance evidence-based dementia palliative care interventions. DEM-PCCT represents the first national program to integrate dementia-specific palliative care training with structured grant development, NIH stage model-based didactics, experiential trainings and longitudinal evaluation of scholar productivity-addressing a critical gap. METHODS: DEM-PCCT is a ten-month program where scholars participate in monthly virtual sessions and a one-week in-person didactic and experiential program. The curriculum to supports participants' research grant development and submission. Participants evaluate curriculum components and report their confidence, comfort and knowledge with conducting dementia clinical trials pre- and post-training. We also track grant submissions, published manuscripts and feasibility outcomes. RESULTS: Three cohorts of interdisciplinary scholars (N = 53) have started and two have completed DEM-PCCT. Program feasibility and satisfaction were high. Confidence and comfort conducting dementia clinical trials significantly improved (P-value <0.05 for both). Most scholars submitted at least one research grant application by program completion, and secured funding. Scholars report continued productivity in grants and publications. CONCLUSION: DEM-PCCT is a novel national training model that advances dementia palliative care by combining didactic training, experiential learning and structured grant development. Thus, DEM-PCCT builds the scientific workforce and serves as a model to accelerate evidence-based dementia palliative care interventions.
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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 | 0.123 | 0.160 |
| 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 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".