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Record W3011682540 · doi:10.1017/s1478951520000164

Portuguese Patient Dignity Question: A cross-sectional study of palliative patients cared for in primary care

2020· article· en· W3011682540 on OpenAlexaff
Mafalda Lemos Caldas, Miguel Julião, Ana João Santos, Harvey Max Chochinov

Bibliographic record

VenuePalliative & Supportive Care · 2020
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsResearch Institute in Oncology and HematologyCancerCare Manitoba
Fundersnot available
KeywordsDignityPalliative careMedicineContext (archaeology)PersonhoodNursingFamily medicineCross-sectional study

Abstract

fetched live from OpenAlex

INTRODUCTION: The Patient Dignity Question (PDQ) is a clinical tool developed with the aim of reinforcing the sense of personhood and dignity, enabling health care providers (HCPs) to see patients as people and not solely based on their illness. OBJECTIVE: To study the acceptability and feasibility of the Portuguese version of the PDQ (PDQ-PT) in a sample of palliative care patients cared for in primary care (PC). METHOD: A cross-sectional study using 20 palliative patients cared for in a PC unit. A post-PDQ satisfaction questionnaire was developed. RESULTS: Twenty participants were included, 75% were male; average age was 70 years old. Patients found the summary accurate, precise, and complete; all said that they would recommend the PDQ to others and want a copy of the summary placed on their family physician's medical chart. They felt the summary heightened their sense of dignity, considered it important that HCPs have access to the summary and indicated that this information could affect the way HCPs see and care for them. The PDQ-PT's took 7 min on average to answer, and 10 min to complete the summary. SIGNIFICANCE OF RESULTS: The PDQ-PT is well accepted and feasible to use with palliative patients in the context of PC and seems to be a promising tool to be implemented. Future trials are now warranted.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

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 head0.072
GPT teacher head0.357
Teacher spread0.285 · 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.

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

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