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Record W3104595899 · doi:10.1080/09699260.2020.1841875

Implementation of an educational intervention pilot for residents on acute care general internal medicine wards around the ‘comfort measures strategy’ for end of life care

2020· article· en· W3104595899 on OpenAlexaff
Lesia Wynnychuk, Damanjot Otal, Heather Davidson, Aakriti Pyakurel, Kalli Stilos

Bibliographic record

VenueProgress in Palliative Care · 2020
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsWestern UniversitySunnybrook Health Science CentreHealth Sciences CentreUniversity of Toronto
Fundersnot available
KeywordsMedicinePalliative careIntervention (counseling)End-of-life careScale (ratio)Randomized controlled trialSelf-efficacyNursingFamily medicinePsychology

Abstract

fetched live from OpenAlex

Purpose This multi-component educational intervention was aimed at General Internal Medicine residents’ perceived self-efficacy in providing end of life care. This study also measured the uptake of the Comfort Measures Order Set. Methods This non-randomized study was conducted over nine 4-week rotations on one General Internal Medicine ward. The intervention consisted of: 1) a didactic module, 2) presence of the Palliative Care Consult Team at General Internal Medicine rounds and, 3) provision of end of life care educational materials. Twenty learners completed a pre/post Self-Efficacy in Palliative Care Scale. Data/Results Data revealed improved self-efficacy ratings on the overall scale, and on all three subscales of the Self-Efficacy in Palliative Care Scale. The Comfort Measures Order Set was implemented in 62% of patient deaths in the intervention group, and 51% of patient deaths in the control group, demonstrating no statistical difference between these groups. Conclusion The uptake of the order set in both the intervention and control groups demonstrated utility in providing a clinical framework for delivering end of life care and highlighted the need for on-going education and enhancement of clinicians' self-efficacy in end of life care.

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)0.000
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 score0.283
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.210
GPT teacher head0.512
Teacher spread0.302 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProgress in Palliative CareSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207