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Record W2803480821 · doi:10.1177/0733464818776794

A Mixed-Methods Approach to Understanding the Palliative Needs of Parkinson’s Patients

2018· article· en· W2803480821 on OpenAlexaff
Lindsay P. Prizer, Jennifer L. Gay, Mark G. Wilson, Kerstin Gerst Emerson, Anne P. Glass, Janis M. Miyasaki, Molly M. Perkins

Bibliographic record

VenueJournal of Applied Gerontology · 2018
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Alberta
FundersCollege of Public Health
KeywordsPalliative careSpiritualityQuality of life (healthcare)MedicineParkinson's diseaseNeeds assessmentDiseaseSocial needsSocial supportHealth related quality of lifePsychologyGerontologyFamily medicineHealth careNursingAlternative medicinePsychotherapist

Abstract

fetched live from OpenAlex

Parkinson's disease (PD) is the second-most common age-related neurodegenerative disorder. Despite recommendations for a palliative approach, little is known about what palliative needs are unmet by standard care. This study aims to (a) identify palliative needs of PD patients, (b) determine the relationship between palliative needs and health-related quality of life (HRQoL), and (c) probe into factors affecting HRQoL. PD patients and neurologists were recruited for a survey on palliative need; a subset of patients was interviewed. Significant differences between physicians and patients were found in Physical, Psychological, Social, Financial, and Spiritual domains. Physical and Psychological needs predicted HRQoL. Primary themes across interviews included (a) lack of healthcare education and (b) need for care coordination. Secondary themes included (a) the importance of support groups, (b) the role of spirituality/religion, and (c) the narrow perceived role of the neurologist. Findings highlight the importance of coordinated individualized 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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.230
GPT teacher head0.438
Teacher spread0.208 · 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 designQualitative
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

Citations18
Published2018
Admission routes1
Has abstractyes

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