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Developing and Testing a Program to Strengthen the Dementia Palliative Care Trial Workforce

2025· article· en· W7117547839 on OpenAlexaff
Ana‐Maria Vranceanu, Hannah Puttre, Sarah Stone, Kathryn I. Pollak, Jean S. Kutner, Christine Seel Ritchie

Bibliographic record

VenueJournal of Pain and Symptom Management · 2025
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsInstitute of Aging
FundersNational Institute on AgingNational Institutes of HealthGarrison Family Foundation
KeywordsPalliative careDementiaWorkforceExperiential learningMEDLINE

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.123
metaresearch head score (Gemma)0.160
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.123
Threshold uncertainty score0.650

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1230.160
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.003
Scholarly communication0.0070.008
Open science0.0060.014
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0200.004

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.

Opus teacher head0.136
GPT teacher head0.420
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

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Citations0
Published2025
Admission routes1
Has abstractno

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