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Record W3184373089 · doi:10.5737/23688076313298305

The relationships of unmet needs with quality of life and characteristics of Indonesian gynecologic cancer survivors

2021· article· en· W3184373089 on OpenAlexvenueno aff
Yati Afiyanti, Besral Besral, Haryani Haryani, Ariesta Milanti, Lina Anisa Nasution, Kemala Rita Wahidi, Dewi Gayatri

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsGynecologic cancerIndonesianMedicineQuality of life (healthcare)CancerCross-sectional studyGerontologyFamily medicineInternal medicineOvarian cancerNursing

Abstract

fetched live from OpenAlex

Gynecologic cancer survivors' complex needs are too often overlooked. This study aimed to identify the associations between unmet needs and quality of life, and selected characteristics of Indonesian gynecologic cancer survivors. This study was a cross-sectional, correlation study. A total of 298 participants completed the Cancer Survivor Unmet Needs (CaSUN), EORTC QLQ-C30, and demographic and clinical-related questionnaires. A higher level of unmet needs was linked to lower perceived quality of life. Higher levels of unmet needs were associated with younger age, lower income, higher educational background, shorter time since diagnosis, more advanced cancer stage, and having combination therapies (p < 0.05). The most frequently reported unmet need of the Indonesian gynecologic cancer survivors was financial support (70.5%). The gynecologic cancer survivors who had completed primary treatment need continuous comprehensive cancer care to help them cope with the lingering or emerging problems related to cancer and its treatment.

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)0.002
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 score0.667
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.061
GPT teacher head0.344
Teacher spread0.283 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

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