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Record W4407277794 · doi:10.62787/mhm.v3i1.94

Linking Pathways from Perceived Absolute Risk and Social Support to Female Regular Health Screening: Integrated with Social Cognitive Theory Model

2025· article· en· W4407277794 on OpenAlexaff
Sun Hui-wen, Zhenyi Li

Bibliographic record

VenueThe Journal of Medicine Humanity and Media · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsSocial cognitive theoryAbsolute (philosophy)Social supportPsychologyCognitionSocial riskRisk perceptionSocial psychologyMedicinePerceptionEnvironmental healthPsychiatryEpistemology

Abstract

fetched live from OpenAlex

The increasing incidence of cancer among women of childbearing age has emerged as a significant health crisis, necessitating urgent attention from both individuals and societal medical systems. Cancer-related screening and prevention strategies are prevalent public health responses to this challenge. This study integrates the Social Cognitive Theory (SCT) with the Health Belief Model (HBM) and Self-Determination Theory (SDT) to examine the psychological and behavioral factors influencing health outcomes. Specifically, it investigates how perceived absolute risk, self-efficacy, self-regulation, social support, and health screening behaviors interrelate, focusing on their mutual reinforcement in the context of female health regulation. Addressing a gap in existing research on health behavior change in a sustainable loop, this study employs a nationally representative online survey with 904 female participants. Data were analyzed using Structural Equation Modeling (SEM), revealing that the integrated model exhibited a good fit and that all five hypotheses were supported. The findings emphasizing the importance of psychological and behavioral impacts on public health behaviors.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.043
GPT teacher head0.300
Teacher spread0.257 · 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
Published2025
Admission routes1
Has abstractyes

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