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Record W4385851471 · doi:10.2196/46953

Association Between Online Health Information–Seeking Behaviors by Caregivers and Delays in Pediatric Cancer: Mixed Methods Study in China

2023· article· en· W4385851471 on OpenAlexaff
Jiamin Wang, Xuemei Zhen, Peter C. Coyte, Di Shao, Ni Zhao, Lele Chang, Yujia Feng, Xiaojie Sun

Bibliographic record

VenueJournal of Medical Internet Research · 2023
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Natural Science Foundation of ChinaShandong University
KeywordsMedicineDescriptive statisticsLogistic regressionHealth Information National Trends SurveyAssociation (psychology)Pediatric cancerCancerClinical psychologyFamily medicinePsychologyHealth careHealth information

Abstract

fetched live from OpenAlex

BACKGROUND: Pediatric cancer patients in China often present at an advanced stage of disease resulting in lower survival and poorer health outcomes. One factor hypothesized to contribute to delays in pediatric cancer has been the online health information-seeking (OHIS) behaviors by caregivers. OBJECTIVE: This study aims to examine the association between OHIS behaviors by caregivers and delays for Chinese pediatric cancer patients using a mixed methods approach. METHODS: This study used a mixed methods approach, specifically a sequential explanatory design. OHIS behavior by the caregiver was defined as the way caregivers access information relevant to their children's health via the Internet. Delays in pediatric cancer were defined as any one of the following 3 types of delay: patient delay, diagnosis delay, or treatment delay. The quantitative analysis methods included descriptive analyses, Student t tests, Pearson chi-square test, and binary logistic regression analysis, all performed using Stata. The qualitative analysis methods included conceptual content analysis and the Colaizzi method. RESULTS: A total of 303 pediatric cancer patient-caregiver dyads was included in the quantitative survey, and 29 caregivers completed the qualitative interview. Quantitative analysis results revealed that nearly one-half (151/303, 49.8%) of patients experienced delays in pediatric cancer, and the primary type of delay was diagnosis delay (113/303, 37.3%), followed by patient delay (50/303, 16.5%) and treatment delay (24/303, 7.9%). In this study, 232 of the 303 (76.6%) caregiver participants demonstrated OHIS behaviors. When those engaged in OHIS behaviors were compared with their counterparts, the likelihood of patient delay more than doubled (odds ratio=2.21; 95% CI 1.03-4.75). Qualitative analysis results showed that caregivers' OHIS behaviors impacted the cancer care pathway by influencing caregivers' symptom appraisal before the first medical contact and caregivers' acceptance of health care providers' diagnostic and treatment decisions. CONCLUSIONS: Our findings suggest that OHIS among Chinese pediatric caregivers may be a risk factor for increasing the likelihood of patient delay. Our government and society should make a concerted effort to regulate online health information and improve its quality. Specialized freemium consultations provided by health care providers via online health informatic platforms are needed to shorten the time for caregivers' cancer symptom appraisal before the first medical contact.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.083
GPT teacher head0.517
Teacher spread0.433 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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