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Record W3100014281 · doi:10.5216/ree.v22.56605

Toronto’s Portuguese-speaking community potential for creating a social support-networks for breast cancer

2020· article· en· W3100014281 on OpenAlexaffabout
Christine Maheu, Margareth Santos Zanchetta, Abinet Gebreegziabher Gebremariam, Mary Rachel Lam-Kin-Teng

Bibliographic record

VenueRevista Eletrônica de Enfermagem · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsToronto Metropolitan UniversityMcGill University
Fundersnot available
KeywordsPortugueseSocial supportBreast cancerSocial network (sociolinguistics)SociologyGerontologyMedicinePsychologyCancerPolitical scienceSocial psychologySocial media

Abstract

fetched live from OpenAlex

An ethnographic study explored ideas about the possibility of creating social support networks for breast cancer within the Portuguese-speaking community in Toronto (Canada). Nineteen men and women from Angolan, Brazilian and Portuguese communities informed about a social support network with a focus on enabling versus challenging conditions for its construction. The fundamental components in creating social support networks were: the demystification of breast cancer and its prevention, emphasis on health education, mobilizing volunteers and direct social support to women living with breast cancer. The potential enabling factors were the participation of older women as social leaders, and the utilization of schools and religious institutions. Perceived barriers were: breast cancer believed to be women’s disease, lack of knowledge about its cure/ rehabilitation, as well as a limited sensitivity to cancer. Social support networks should consider the communities’ diverse cultural and tangible needs, as well as more informal social support services.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.113
GPT teacher head0.387
Teacher spread0.274 · 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 designOther design
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 routes2
Has abstractyes

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