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Record W2462806780 · doi:10.1177/082585971503100107

Evaluation of the Utility of the Edmonton Symptom Assessment System (revised) Scale on a Tertiary Palliative Care Unit

2015· article· en· W2462806780 on OpenAlexaffabout
Elizabeth Beddard-Huber, Jyothi Jayaraman, Laura White, Wendy Yeomans

Bibliographic record

VenueJournal of Palliative Care · 2015
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsLikert scaleScale (ratio)Palliative careMedicineUnit (ring theory)Family medicineTertiary careNursingEmergency medicinePsychology

Abstract

fetched live from OpenAlex

AIM: The aim of this study was to evaluate the utility of the Edmonton Symptom Assessment System (ESAS-r) Scale on a tertiary palliative care unit. METHOD: There were 92 admitted patients who participated in the study; the scale was administered to those able to participate on day 1 (n = 35, 38 percent), on day 4 (n = 20, 21 percent), and weekly. Patient comfort level with the ESAS-r tool was assessed using a 5-point Likert scale (strongly disagree to strongly agree) on day 4. Nurses' and physicians' perceptions of clinical assessment pre- and postimplementation of the scale were surveyed using a 5-point Likert scale. RESULTS: Of the participating physicians, 75 percent (n = 3) found that the ESAS-r Scale did not enhance clinical assessment; the proportion of nurses with that response was 37.5 percent (n = 6). Among these care providers, 50 percent of the physicians (n = 2) and 62 percent of the nurses (n = 10) thought that the scale was burdensome to patients; but 60 percent of the patients who were able to complete the comfort-level survey (n = 12) indicated that they did not find the scale burdensome. CONCLUSION: Patient acuity, team expertise, perceived burden to patients, and time commitment all influenced staff's recommendation not to implement the ESAS-r tool on the palliative care unit.

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)0.002
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 score0.119
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.233
GPT teacher head0.468
Teacher spread0.235 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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