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Record W2051018313 · doi:10.1348/135910704x14429

Two Black men with prostate cancer: A narrative approach

2005· article· en· W2051018313 on OpenAlexaff
Ross E. Gray, Karen Fergus, Margaret I. Fitch

Bibliographic record

VenueBritish Journal of Health Psychology · 2005
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsProstate cancerNarrativePsychological interventionQualitative researchPsychologyNarrative inquiryPerspective (graphical)Health psychologyClinical psychologyCancerMedicineGerontologyPublic healthSociologyNursingPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: This paper demonstrates the value of a narrative approach for health psychology. It focuses on the lives of two Black men with prostate cancer, drawn from a larger study investigating the links between masculinities and prostate cancer. DESIGN: The study was a qualitative, interview-based study. Each participant was interviewed four times. METHODS AND ANALYSIS: The men were asked to describe and discuss their prostate cancer experience, as well as their lives prior to illness. In order to gain a perspective on individual experiences of men with prostate cancer, we took a narrative approach to gathering and analysing data. Results are reported through two descriptive narratives. CONCLUSIONS: The narratives of the men described in the paper show how the interaction of race with health and illness is neither predictable nor consistent at the individual level. Black men, like all men with prostate cancer, have diverse experiences and are influenced by a wide array of personal and societal factors. While the high risk of prostate cancer among Black men makes proactive interventions advisable, such interventions will be most effective if the heterogeneity of men's experiences are taken into account.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.460

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 integrity0.0000.001
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 head0.026
GPT teacher head0.377
Teacher spread0.351 · 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 designNot applicable
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

Citations39
Published2005
Admission routes1
Has abstractyes

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