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Record W2763383875 · doi:10.1016/j.jana.2017.10.002

Complex Problems, Care Demands, and Quality of Life Among People Living With HIV in the Antiretroviral Era in Indonesia

2017· article· en· W2763383875 on OpenAlexaff
Linlin Lindayani, Yen‐Chin Chen, Jung‐Der Wang, Nai‐Ying Ko

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPsychosocialPalliative careReferralMedicineQuality of life (healthcare)Cronbach's alphaPsychological interventionHuman immunodeficiency virus (HIV)GerontologyDistressCross-sectional studyFamily medicineSocial supportPsychiatryNursingPsychologyClinical psychologyPsychometrics

Abstract

fetched live from OpenAlex

People living with HIV (PLWH) suffer from physical and psychological distress that palliative care could alleviate. Our cross-sectional study identified HIV-related problems and demands for palliative care at different disease stages, and their interactions with quality of life (QOL) in 215 PLWH from a referral hospital and an AIDS nongovernmental organization in Indonesia. A brief survey of demographic information, the Bahasa version of Problems and Needs of Palliative Care, and the World Health Organization Quality of Live in HIV-infected Persons instrument (WHOQOL-HIV BREF; Cronbach's alpha = .89) were used for data collection. Mean age was 33.5 years (SD = 4.7); 66% were male. Fatigue (67%) was the most prevalent symptom, and the symptom sleeping problems (54.9%) was the priority for palliative care. Higher spiritual and financial demands were found in PLWH with stage IV HIV. Multivariable analysis indicated negative associations between QOL and psychosocial problems, and demands for social and financial support. Interventions focused on psychosocial issues would improve the QOL for PLWH.

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)0.003
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 score0.015
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.342
Teacher spread0.316 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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