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Record W2771146393 · doi:10.5195/hcs.2017.213

Meanings of Breast Cancer Survivorship Among Members of Ethnically-Identified Support Groups

2017· article· en· W2771146393 on OpenAlexfundno aff
Karen E. Dyer, Jeannine Coreil

Bibliographic record

VenueHealth Culture and Society · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsSurvivorship curveCancer survivorshipParallelsGender studiesFaithIdentity (music)OptimismSociologyPsychologySocial psychologyCancerMedicineAesthetics

Abstract

fetched live from OpenAlex

Research on both cancer survivorship and support needs has been limited in its attention to survivors from culturally-diverse communities. This study examined the perspectives of members and leaders of ethnically-identified breast cancer support groups regarding the meanings and expectations attached to survivorship. Semi-structured interviews were conducted with 38 African American and Latina survivors in Central Florida. Participant narratives invoked themes of spiritual renewal and deepening religious faith, and deemphasized individual responsibility for personal change. Participants emphasized the importance of shared cultural identity in shaping the survivor experience, and some Latina women drew parallels between survivorship and the challenges of migration to a new country. An unwavering display of optimism was held to be paramount. These themes are interpreted within the framework of the interplay between dominant societal discourses of survivorship and locally-constructed meanings. Findings underscore the importance for healthcare providers to be cognizant and respectful of diverse perspectives on illness.

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 score0.183
Threshold uncertainty score0.994

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 head0.028
GPT teacher head0.339
Teacher spread0.311 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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