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Record W4394689583 · doi:10.32920/25569771

A cross-cultural analysis of salivary cortisol patterns in breast cancer survivors

2024· preprint· en· W4394689583 on OpenAlexaff
Cynthia Wan, Kayla Boileau, Danielle D’Amico, Vivian Huang, Alexandra Fiocco, Richard Clément, Catherine Bielajew

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsToronto Metropolitan UniversityUniversity of Ottawa
Fundersnot available
KeywordsBreast cancerOncologyInternal medicineMedicinePsychologyCancerEndocrinology

Abstract

fetched live from OpenAlex

Aim: In this study, we examined whether Chinese and White women with and without a history of breast cancer exhibit differences in physiological and psychological stress profiles. Methods: Diurnal and reactive salivary cortisol profiles and psychological stress patterns of 41 breast cancer survivors and 58 healthy women were assessed. Results: Breast cancer survivors displayed a blunted acute cortisol response but there was no main effect of ethnocultural membership. Subjective appraisals of stress during the acute stressor revealed a significant interaction between ethnocultural group, health status and time (p = 0.032). Conclusion: Our results support the existing literature though suggest group differences in the appraisal of stress; thus, underscoring the importance of cultural sensitivity and awareness among clinicians and existing programs.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.034
GPT teacher head0.456
Teacher spread0.422 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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