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Record W1673431772 · doi:10.14740/wjon928w

Unmet Supportive Cancer Care Needs: An Exploratory Quantitative Study in Rural Australia

2015· article· en· W1673431772 on OpenAlexvenueno aff
Krishna Rachakonda, Mathew George, Mohsen Shafiei, Christopher Oldmeadow

Bibliographic record

VenueWorld Journal of Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineExploratory researchCancerFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is a discernible, often ignored under-evaluated care-management gap in supportive cancer care, where the estimated clinical outcome is seldom translated into patient-centered benefit. METHODS: The present research is an exploratory cross-sectional quantitative questionnaire survey study done in rural regions of Australia with the sole purpose of evaluating the care-management gap in terms of the unmet supportive needs of advanced cancer patients to provide baseline data for planning, drafting and implementing innovative and effective supportive care services that will address the specific priorities and unmet needs identified in this vulnerable population in the remote and rural regions. RESULTS: The questionnaire (NA-ACP) was comprised of 132 questions covering seven domains of supportive care. Three centers in rural regions of Australia were selected for the study. While center 1 had medical and surgical specialties, centers 2 and 3 were outreach oncology clinics with nurse-led chemotherapy units. A total sample of 75 patients getting continuous treatment procedures at these three oncology units was given the NA-ACP questionnaire. CONCLUSION: The data from this study can be used to improve and inform care for this population by identifying specific unmet supportive 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.130
GPT teacher head0.439
Teacher spread0.310 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

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