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Record W3034107158 · doi:10.2196/16138

Beliefs and Information Seeking in Patients With Cancer in Southwest China: Survey Study

2020· article· en· W3034107158 on OpenAlexvenueno aff
Juan Xie, Shi Xie, Ying Cheng, Zhe He

Bibliographic record

VenueJMIR Cancer · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersNanjing University
KeywordsChinaCancerInformation seekingPsychologyGeographyFamily medicineMedicineInformation retrievalComputer scienceArchaeologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Although previous studies have reported the cancer information-seeking behaviors among patients in high-income countries, the cancer information-seeking practices of patients living in low- and middle-income areas are less known. OBJECTIVE: This study investigated the beliefs and information-seeking patterns of cancer patients in southwest China. METHODS: A questionnaire was designed, and data were collected in two hospitals (N=285) in southwest China. Statistical analyses included bivariate analyses and regressions. RESULTS: Patients' attitudes towards cancer fatalism were significantly influenced by marital status (P<.001), education (P<.001), and household income (P<.001). Moreover, endorsing fatalistic belief was positively associated with age (r=0.35, P<.001). The regression model showed that younger patients (odds ratio [OR] 0.96, 95% CI 0.93-0.99) and those with higher education (OR 1.75, 95% CI 1.09-2.81) were more likely to seek information. Additionally, patients who were less confident in getting information were more likely to find information (OR 1.70, 95% CI 1.15-2.52), while fatalism belief was not significant in the regression (OR 0.65, 95% CI 0.22-1.95). CONCLUSIONS: This study explored the information-seeking patterns of cancer patients in southwest China. It was found that many Chinese people endorsed cancer fatalism. These pessimistic beliefs about the potential to prevent and to cure cancer correlate with rather than cause cancer-related information seeking. However, self-efficacy about the confidence in finding needed cancer information was a significant predictor of information-seeking.

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)0.000
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 score0.012
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.321
Teacher spread0.285 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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