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Record W4321504867 · doi:10.1177/23337214231158470

Palliative Approach to Care Education for Multidisciplinary Staff of Long-Term Care Homes: A Pretest Post-Test Study

2023· article· en· W4321504867 on OpenAlexafffund
Shirin Vellani, Vanessa Maradiaga Rivas, Maria Nicula, Stephanie Pedrotti Lucchese, Julia Kruizinga, Tamara Sussman, Sharon Kaasalainen

Bibliographic record

VenueGerontology and Geriatric Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcGill UniversityMcMaster University
FundersCanadian Frailty Network
KeywordsMultidisciplinary approachPalliative careAdvance care planningNursingIntervention (counseling)Test (biology)MedicinePerceptionLong-term carePsychologyFamily medicine

Abstract

fetched live from OpenAlex

This study used a single-group pre-test and post-test design to evaluate an educational workshop for multidisciplinary staff working in long-term care homes on implementing a palliative approach to care and perceptions about advanced care planning conversations. Two outcomes were measured to assess the preliminary efficacy of the educational workshop at baseline and 1-month post-intervention. Knowledge regarding implementing a palliative approach to care was assessed using the End-of-Life Professional Caregivers Survey and changes in staff perception toward ACP conversations were assessed using the Staff Perceptions Survey. Findings suggest that staff experienced an improvement in self-reported knowledge regarding a palliative approach to care ( p ≤ .001); and perceptions of knowledge, attitude, and comfort related to advance care planning discussions ( p ≤ .027). The results indicate that educational workshops can assist in improving multidisciplinary staff’s knowledge about a palliative approach to care and comfort in carrying out advance care planning discussions with residents, family care partners, and among long-term care staff.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.433
Teacher spread0.341 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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