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Record W2990779005

Patient Dignity Question: Feasible, dignity-conserving intervention in a rural hospice.

2019· article· en· W2990779005 on OpenAlexaffabout
Pamela McDermott

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsNOSM University
Fundersnot available
KeywordsDignityCompassionPalliative careIntervention (counseling)NursingMedicineScale (ratio)Death with dignityFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the feasibility of using the Patient Dignity Question (PDQ) in a small rural hospice setting. DESIGN: Prospective study. SETTING: The 5-bed Algonquin Grace Hospice in Huntsville, Ont. PARTICIPANTS: Nineteen patients who met the research criteria and who were admitted to the hospice from September 2015 to December 2016. METHODS: Participants completed the Patient Dignity Inventory and modified versions of the Edmonton Symptom Assessment Scale and Integrated Palliative Care Outcome Scale before and after the PDQ interviews. MAIN FINDINGS: While each of the 19 PDQ interviews was unique, there were many consistencies regarding accomplishments (eg, being a good parent), hopes (eg, one's need of being respected), and fears (eg, concerns about pets) that emerged from participants' stories. Hospice staff found the documents from PDQ interviews to be very valuable in their understanding of patients. Staff and patients unanimously wanted the program to continue. An unexpected benefit was the response of the patients' families who were deeply moved by the legacy documents, often sharing them following their family member's death. CONCLUSION: The PDQ is a dignity-conserving intervention that serves as a meaningful end-of-life legacy document that benefits patients, staff, and families. Using the PDQ at the hospice helped patients feel truly heard, and increased caregivers' compassion and understanding of patients' needs.

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 score0.186
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.027
GPT teacher head0.262
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

Citations5
Published2019
Admission routes2
Has abstractyes

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