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Record W4316174023 · doi:10.5281/zenodo.7537225

Quality of life of women with breast cancer in Kinshasa and its explanatory factors

2023· article· en· W4316174023 on OpenAlexaboutno aff
Jonathan Enguta Mwenzi

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerQuality (philosophy)Explanatory modelMedicineQuality of life (healthcare)DemographyCancerInternal medicineStatisticsMathematicsSociologyPhilosophyNursing

Abstract

fetched live from OpenAlex

<strong>Introduction:</strong> The main objective of this study was to assess the quality of life of women with breast cancer in Kinshasa in order to identify the repercussions of this pathology on the mental life of these women. In addition, the study aims to determine the effect of socio-demographic variables on the quality of life of these women. <strong>Methods:</strong> The study sample consisted of 61 women with breast cancer treated at the University Clinics of Kinshasa. The McGill Quality of Life Rating Scale was administered to the study subjects. The survey took place throughout the month of July 2021. <strong>Results:</strong>women with breast cancer show a state of general and physical ill-being. They experience a strong sense of psychological, emotional and social well-being. The number of children variable influenced the psychological well-being of these women. The cancer stage variable influenced the psychological well-being of these women. <strong>Conclusions:</strong> the breast cancer suffered by these women negatively affects their general and physical well-being. The psychological, emotional and social well-being of these women is not affected, and this, because of the integral care at the University Clinics of Kinshasa which contributes to the restoration of the affects of the latter.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.068
GPT teacher head0.308
Teacher spread0.240 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicFamily Support in IllnessFrench-language works237,207