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Record W2968061863 · doi:10.2196/14241

Health-Related Internet Use Among Men With Prostate Cancer in Canada: Cancer Registry Survey Study

2019· article· en· W2968061863 on OpenAlexafffundabout
Jacqueline L. Bender, Deb Feldman‐Stewart, Christine Tong, Karen Lee, Michael Brundage, Howard Pai, John W. Robinson, Tony Panzarella

Bibliographic record

VenueJournal of Medical Internet Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of CalgaryPublic Health OntarioUniversity of British ColumbiaQueen's UniversityPrincess Margaret Cancer CentreUniversity of Toronto
FundersProstate Cancer CanadaMovember Foundation
KeywordsProstate cancerCancer registryMedicineThe InternetCancerFamily medicineGerontologyWorld Wide WebInternal medicineComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: After a prostate cancer diagnosis, men want information about their disease and treatment options. The internet offers a convenient means to deliver health information to patients with prostate cancer. However, there are concerns about the use of the internet among this largely senior population. OBJECTIVE: This study aimed to determine the patterns and factors associated with the use of the internet as a source of health information among Canadian men with prostate cancer and the features and information required in a website. METHODS: Population surveys were conducted in four Canadian provinces (British Columbia, Alberta, Saskatchewan, and Ontario) in 2014-2015. Data analyses included descriptive, bivariable, and multivariable analyses. The Pearson Chi-square and univariable regression were used to examine associations between independent variables and health-related internet use. Correlates of health-related internet use were analyzed using multivariable logistic regression. RESULTS: A total of 1362 patients responded across the four provinces. The mean age of respondents was 69 years (SD 8.2). In addition, 82% (n=1071) were internet users and 71% (n=910) used the internet daily. Further, 65% (n=784) used the internet as a source of prostate cancer information, and 40% (n=521) were confident about using information obtained from the internet to make health decisions. Men who used the internet to obtain prostate cancer information were more likely to be active information seekers (odds ratio [OR]: 4.5, 95% CI 2.6-7.8), be confident using information from the internet to make health decisions (OR: 3.6, 95% CI 2.3-5.7), have broadband internet access (OR: 1.8, 95% CI 1.2-2.7), and have more unmet supportive care needs (OR: 1.05, 95% CI 1.0-1.1). Top features wanted in a website, reported by more than 50% of respondents, were a library of resources (n=893, 65.6%), tools to support treatment decision making (n=815, 59.8%), and tools to help navigate the prostate cancer journey (n=698, 51.2%). Top three topics of information wanted in such a website were treatment options (n=916, 67.3%), disease progression (n=904, 66.4%), and management of side effects (n=858, 63%). CONCLUSIONS: Over two-thirds of Canadian patients with prostate cancer surveyed use the internet as a source of health information about prostate cancer, but over half did not feel confident using information from the internet to make health decisions. Being an active information seeker, having confidence in using information from the internet to make health decisions, having broadband internet, and having more unmet supportive care needs were significantly associated with health-related internet use. Future work should examine electronic health literacy interventions as a means to boost men's confidence in using information from the internet and design websites that include information and features that help men navigate the prostate cancer journey and support treatment decision making and management of side effects.

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)0.034
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0340.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.007
Insufficient payload (model declined to judge)0.0090.000

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 head0.131
GPT teacher head0.528
Teacher spread0.398 · 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.

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

Citations35
Published2019
Admission routes3
Has abstractyes

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