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Record W2914937580 · doi:10.5430/jst.v9n1p33

Assessment of the impact of breast cancer on women in Gombe State, Northeastern Nigeria

2019· article· en· W2914937580 on OpenAlexvenueno aff
Jonah H. Japhet, Dathini Hamina, Doka J. S. Pauline, Kever Robert Teryila, Habu Haruna, Uba M. Njida, Emma Yagana, Langa Mshelia

Bibliographic record

VenueJournal of Solid Tumors · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsNonprobability samplingBreast cancerSpouseThematic analysisQualitative researchMedicineGovernment (linguistics)CancerPsychologyFamily medicineEnvironmental healthPolitical sciencePopulationSociologySocial science

Abstract

fetched live from OpenAlex

Objective: The aim of the study is to assess the physical, financial, emotional and social impact of breast cancer on women in North east Nigeria.Methods: A qualitative transcendental phenomenological study design was adopted, using face-to face unstructured interview to collect data from 22 respondents who were recruited via purposive non-probability sampling technique. The interviews were recorded, transcribed verbatim and further analyzed using thematic analysis method into themes and sub-themes.Results: The result revealed excruciating pains that does not abate completely as the main physical impact, expensive cost of treatment as the major financial impact and crying as the major emotional impact of breast cancer on women. However, been diagnosed with breast cancer had no any consequences on participants’ relationship with spouse or family members.Conclusion: Breast cancer causes pain, makes women to cry and the treatment is very expensive but it does not cause relationship problems. There is therefore the need for Government and Non-governmental policies to be geared towards supporting women with breast cancer to overcome these challenges.

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 score0.015
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.366
Teacher spread0.343 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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