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Record W7113457014

Communcation Matters: Mother-Daughter Communication in Breast Cancer Prevention in Taiwan

2020· article· en· W7113457014 on OpenAlexaboutno aff

Bibliographic record

VenueUNI ScholarWorks (University of Northern Iowa) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerCancerCancer preventionQualitative researchQuarter (Canadian coin)Cancer registryHealth communication
DOInot available

Abstract

fetched live from OpenAlex

Open mother-daughter communication may enhance cancer knowledge, awareness, and prevention behaviors among the family members of cancer survivors. In Taiwan, almost one quarter (24.7 %) of all cancer diagnoses among females are attributed to breast cancer. This study, whose maternal participants are Taiwanese breast cancer survivors, investigated the potential influence of mother-daughter cancer communication on their daughters’ cancer prevention awareness, attitudes and behaviors. The research design employed a concurrent quantitative-dominant mixed method design in which both quantitative and qualitative data were collected and analyzed at the same time. The qualitative research consisted of seven in-depth interviews with daughters of breast cancer survivors and the quantitative research consisted of a survey of eighty daughters of breast cancer survivors. Results reveal that mother-daughter relationships and their communication were influenced by the mothers’ dependency on and accessibility to their daughters. Additionally, subsequent cancer prevention behaviors by the daughters were significantly influenced by their mothers’ advice. Open communication from Taiwanese mothers with breast cancer is an important means of cancer prevention among their daughters. The Taiwanese government and health professionals should find ways to encourage such communication and make it an integral part of broader cancer control efforts.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.261
Teacher spread0.236 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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