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Record W3013090947 · doi:10.1136/bmjopen-2019-035173

Experiences of accessing and using breast cancer services in Vietnam: a descriptive qualitative study

2020· article· en· W3013090947 on OpenAlexfundno aff
Chris Jenkins, Tran Thu Ngan, Nguyen Bao Ngoc, Hồ Thị Hiền, Nguyễn Hoàng Anh, Lynne Lohfeld, Michael Donnelly, Hoàng Văn Minh, Liam Murray

Bibliographic record

VenueBMJ Open · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersMedical Research CouncilQueen's UniversityQueen's University BelfastNewton Fund
KeywordsBreast cancerMedicineFamily medicineQualitative researchPublic healthCancerDescriptive researchHealth careDescriptive statisticsGerontologyNursingEconomic growthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To understand, describe and analyse the experiences of women with breast cancer in Vietnam when accessing and using breast cancer services. DESIGN: Descriptive qualitative study. Women were interviewed about their experiences from the first time they became aware of symptoms or changes to their body through treatment and post-treatment. This study is the first descriptive study on breast cancer in Vietnam from the perspective of women with a breast cancer diagnosis. PARTICIPANTS: Women (n=13) who had completed or were still receiving treatment for breast cancer, purposively recruited from the north and south of Vietnam. RESULTS: An analysis of the experiences of women with breast cancer in Vietnam revealed a lack of awareness and knowledge about breast cancer and symptoms. Family and social support were described as key factors influencing whether a woman accesses and uses breast cancer services. Cost of treatment and out-of-pocket expenditures limited access to services and resulted in significant financial challenges for women and their families. CONCLUSIONS: Vietnam has made huge strides in improving cancer care, and is tackling a complex and expanding public health challenge, however, there are a number of areas requiring strengthening and future research. While Vietnam has successfully expanded social health insurance coverage, changes that increase the percentage of costs covered for specific treatments, such as chemotherapy or radiotherapy, could benefit women and their families.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.379
GPT teacher head0.536
Teacher spread0.157 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2020
Admission routes1
Has abstractyes

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