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Record W4385851116 · doi:10.2196/43345

Using the Multidimensional Health Locus of Control Scale Form C to Investigate Health Beliefs About Bladder Cancer Prevention and Treatment Among Male Patients: Cross-Sectional Study

2023· article· en· W4385851116 on OpenAlexvenueno aff
Zhaoquan Xing, Meng Ji, Yi Shan, Zhaogang Dong, Xiaofei Xu

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsLocus of controlHealth literacyBladder cancerMedicineCancer preventionPsychological interventionScale (ratio)Health Information National Trends SurveyClinical psychologyGerontologyPsychologyCancerHealth careInternal medicinePsychiatryDevelopmental psychologyHealth information

Abstract

fetched live from OpenAlex

BACKGROUND: Bladder cancer is a leading cause of death among Chinese male populations in recent years. The health locus of control construct can mediate health status and outcomes, and it has proven helpful in predicting and explaining specific health-related behaviors. However, it has never been used to investigate health beliefs about bladder cancer prevention and treatment. OBJECTIVE: This study aimed to classify male patients into different latent groups according to their beliefs about bladder cancer prevention and treatment and to identify associated factors to provide implications for the delivery of tailored education and interventions and the administration of targeted prevention and treatment. METHODS: First, we designed a four-section questionnaire to solicit data: section 1-age, gender, and education; section 2-the communicative subscale of the All Aspects of Health Literacy Scale; section 3-the eHealth Literacy Scale; and section 4-health beliefs about bladder cancer prevention and treatment measured by the Multidimensional Health Locus of Control Scale Form C. We hypothesized that the participants' health beliefs about bladder cancer prevention and treatment measured in section 4 could be closely associated with information collected through sections 1 to 3. We recruited 718 Chinese male patients from Qilu Hospital of Shandong University, China, and invited them to participate in a web-based questionnaire survey. Finally, we used latent class analysis to identify subgroups of men based on their categorical responses to the items on the Multidimensional Health Locus of Control Scale Form C and ascertained factors contributing to the low self-efficacy group identified. RESULTS: We identified 2 subgroups defined as low and moderate self-efficacy groups representing 75.8% (544/718) and 24.2% (174/718) of the total sample, respectively. Men in the low self-efficacy cluster (cluster 1: 544/718, 75.8%) were less likely to believe in their own capability or doctors' advice to achieve optimal outcomes in bladder cancer prevention and treatment. Men in the moderate self-efficacy cluster (cluster 2: 174/718, 24.2%) had distinct psychological traits. They had stronger beliefs in their own capability to manage their health with regard to bladder cancer prevention and treatment and moderate to high levels of trust in health and medical professionals and their advice to achieve better prevention and treatment outcomes. Four factors contributing to low self-efficacy were identified, including limited education (Year 6 to Year 12), aged ≥44 years, limited communicative health literacy, and limited digital health literacy. CONCLUSIONS: This was the first study investigating beliefs about bladder cancer prevention and treatment among Chinese male patients. Given that bladder cancer represents a leading cause of death among Chinese male populations in recent years, the low self-efficacy cluster and associated contributing factors identified in this study can provide implications for clinical practice, health education, medical research, and health policy-making.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.100
GPT teacher head0.475
Teacher spread0.375 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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