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Record W2082849490 · doi:10.3747/co.v16i4.375

Perceptions of Health Care Providers Concerning Patient and Health Care Provider Strategies to Limit Out-of-Pocket Costs for Cancer Care

2009· article· en· W2082849490 on OpenAlexafffundvenueabout
Maria Mathews, S.K. Buehler, Roianne West

Bibliographic record

VenueCurrent Oncology · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health ResearchNewfoundland and Labrador Centre for Applied Health Research
KeywordsMedicineHealth careMedical prescriptionFamily medicineDieticiansNursingCancer

Abstract

fetched live from OpenAlex

OBJECTIVE: We aimed to describe the perceptions of health care providers concerning patient and health care provider strategies to limit out-of-pocket costs for cancer care. METHODS: We conducted semi-structured interviews with 21 cancer care providers (nurses, social workers, oncologists, surgeons, pharmacists, and dieticians) in Newfoundland and Labrador. RESULTS: Patients try to minimize costs by substituting or rationing medications, choosing radical treatments, lengthening the time between follow-up appointments, choosing inpatient care, and working during treatment to minimize loss of income. Providers respond to the financial concerns of patients by helping them to access financial assistance programs, by changing chemotherapy and supportive drug prescriptions, and by shortening radiation treatment protocols. They admit patients to hospital and arrange follow-up with physicians closer to a patient's home. CONCLUSIONS: Out-of-pocket costs resulting from cancer care are incurred at all phases of treatment and follow-up. These costs are substantial concerns for some patients and their health care providers. Encouraging communication between patients and their providers is needed to identify individuals at risk and to safely modify care plans. Tele-oncology and public drug, medical travel, and leave programs are needed to ensure that patients are better able to afford the costs related to cancer 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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.137
GPT teacher head0.404
Teacher spread0.267 · 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 designOther design
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

Citations32
Published2009
Admission routes4
Has abstractyes

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