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Record W1860225157 · doi:10.1016/j.ijans.2017.04.002

An integrative review of the Literature on the determinants of health outcomes of women living with breast cancer in Canada and Nigeria from 1990 to 2014: A comparative study

2017· article· en· W1860225157 on OpenAlexaffabout
Agatha Ogunkorode, Lorraine Holtslander, June Anonson, Johanna E. Maree

Bibliographic record

VenueInternational Journal of Africa Nursing Sciences · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineBreast cancerTraditional medicineCancerAlternative medicineFamily medicineGerontologyGynecologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Background and Aim: Globally, breast cancer is the most common cancer among women. The stage of the disease at diagnosis is a core determinant of its health outcome. In low to middle-income countries like Nigeria, advanced stage of disease presentation for medical care represents a significant problem. While mortality rates from breast cancer are declining in developed countries like Canada, they are increasing in developing countries like Nigeria. It is well documented that presentation for medical care at the early stages of the disease improves outcome. Knowledge of the factors that impact seeking medical care after breast cancer symptom discovery in women and knowledge of the factors that impact participation in breast health activities by women is important in reducing breast cancer-related mortality. Methods and Design: This integrative review critically examined the determinants of health outcomes of women living with breast cancer in Canada and Nigeria from 1990-2014. Specifically, it examined the factors that impact seeking medical care after breast cancer symptom discovery in women. It also explored the factors that impact participation in breast health activities by women in the two countries from 1990 to 2014. A total of 303 articles were identified and retrieved by searching the following databases: CINAHL, MEDLINE, and EMBASE. Grey literature from relevant organizations websites were identified using Google Scholar. Among the 303 articles identified, 55 met the inclusion criteria. Results and Conclusion: Findings from the articles that met the inclusion criteria showed that Canadians have a high level of breast health awareness. The findings also suggest that women in Nigeria have rather poor knowledge of breast health awareness and breast cancer. In Nigeria, presentation with an advanced stage of the disease made survival very low. This also compromises the quality of life of the patients. The major factors responsible for the late presentations were a lack of breast cancer awareness and education. Other social factors that mitigate against early presentations for medical care include misconceptions about breast cancer treatment and outcomes. In line with the findings of this study, it is recommended that wide spread culturally sensitive, linguistically appropriate, health education programs to teach breast health awareness should be developed and disseminated. Such health awareness programs should be targeted at women through various channels such as the media, the television, and radio. Also, within the hospital, the developed education programs should be integrated into the existing women health education programs. Non-government and other charitable organizations can also make significant contributions to breast health awareness through sponsoring health talks and workshops targeted at relevant segments of the population. Key search words: Breast cancer, breast neoplasm, diagnosis, prevention and control, health knowledge, patient attitude and practice, breast self-examination, awareness, patient education as topic, mass screening, early detection of cancer, Nigeria, Canada.

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)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.740
Threshold uncertainty score0.516

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.014
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.056
GPT teacher head0.417
Teacher spread0.361 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2017
Admission routes2
Has abstractno

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