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Record W1451211093 · doi:10.4018/ijrcm.2015070104

Ethics, Risk, and Media Intervention

2015· article· en· W1451211093 on OpenAlexafffund
Mahmoud M. A. Eid, Isaac Nahón-Serfaty

Bibliographic record

VenueInternational Journal of Risk and Contingency Management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsBreast cancerHealth careMedicinePsychological interventionFamily medicineNursingCancerPublic relationsPolitical scienceInternal medicineLaw

Abstract

fetched live from OpenAlex

Breast cancer incidence and mortality rates are of concern among Latin American women, mainly due to the growing prevalence of this disease and the lack of compliance to proper breast cancer screening and treatment. Focusing on Venezuelan women and the challenges and barriers that interact with their health communication, this paper looks into issues surrounding women's breast cancer, such as the challenges and barriers to breast cancer care, the relevant ethics and responsibilities, the right to health, breast cancer risk perception and risk communication, and the media interventions that affect Venezuelan women's perceptions and actions pertaining to this disease. In particular, it describes an action-oriented research project in Venezuela that was conducted over a four-year period of collaborative work among researchers, practitioners, NGOs, patients, journalists, and policymakers. The outcomes include positive indications on more effective interactions between physicians and patients, increasing satisfactions about issues of ethical treatment in providing healthcare services, more sufficient and responsible media coverage of breast cancer healthcare services and information, a widely supported declaration for a national response against breast cancer in Venezuela, and the creation of a code of ethics for the Venezuelan NGO that led the expansion of networking in support of women's breast cancer healthcare.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.043
GPT teacher head0.329
Teacher spread0.287 · 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 designNot applicable
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

Citations2
Published2015
Admission routes2
Has abstractyes

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