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Record W4366121791 · doi:10.2196/44339

Factors Associated With Online Patient-Provider Communications Among Cancer Survivors in the United States During COVID-19: Cross-sectional Study

2023· article· en· W4366121791 on OpenAlexvenueno aff
Jiyeong Kim, Eleni Linos, Debra A Fishman, Melanie S. Dove, Jeffrey S. Hoch, Theresa H.M. Keegan

Bibliographic record

VenueJMIR Cancer · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHealth Information National Trends SurveyLogistic regressionOddsCross-sectional studyCancerOdds ratioFamily medicineCoronavirus disease 2019 (COVID-19)Health careGerontologyDemographyInternal medicineHealth informationDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Online patient-provider communication (OPPC) is crucial in enhancing access to health information, self-care, and related health outcomes among cancer survivors. The necessity of OPPC increased during SARS-CoV-2/COVID-19, yet investigations in vulnerable subgroups have been limited. OBJECTIVE: This study aims to assess the prevalence of OPPC and sociodemographic and clinical characteristics associated with OPPC among cancer survivors and adults without a history of cancer during COVID-19 versus pre-COVID-19. METHODS: Nationally representative cross-sectional survey data (Health Information National Trends Survey 5, 2017-2020) were used among cancer survivors (N=1900) and adults without a history of cancer (N=13,292). COVID-19 data included data from February to June 2020. We calculated the prevalence of 3 types of OPPC, defined as using the email/internet, tablet/smartphone, or electronic health record (EHR) for patient-provider communication, in the past 12 months. To investigate the associations of sociodemographic and clinical factors with OPPC, multivariable-adjusted weighted logistic regression was performed to obtain odds ratios (ORs) and 95% CIs. RESULTS: The average prevalence of OPPC increased from pre-COVID to COVID among cancer survivors (39.7% vs 49.7%, email/internet; 32.2% vs 37.9%, tablet/smartphone; 19.0% vs 30.0%, EHR). Cancer survivors (OR 1.32, 95% CI 1.06-1.63) were slightly more likely to use email/internet communications than adults without a history of cancer prior to COVID-19. Among cancer survivors, the email/internet (OR 1.61, 95% CI 1.08-2.40) and EHRs (OR 1.92, 95% CI 1.22-3.02) were more likely to be used during COVID-19 than pre-COVID-19. During COVID-19, subgroups of cancer survivors, including Hispanics (OR 0.26, 95% CI 0.09-0.71 vs non-Hispanic Whites) or those with the lowest income (US $50,000-<US $75,000: OR 6.14, 95% CI 1.99-18.92; ≥US $75,000: OR 0.42, 95% CI 1.56-11.28 vs <US $20,000), with no usual source of care (OR 6.17, 95% CI 2.12-17.99), or reporting depression (OR 0.33, 95% CI 0.14-0.78) were less likely to use email/internet, and those who were the oldest (age 35-49 years: OR 9.33, 95% CI 2.18-40.01; age 50-64 years: OR 3.58, 95% CI 1.20-10.70; age 65-74 years: OR 3.09, 95% CI 1.09-8.76 vs age≥75 years), were unmarried (OR 2.26, 95% CI 1.06-4.86), or had public/no health insurance (Medicare, Medicaid, or other: ORs 0.19-0.21 vs private) were less likely to use a tablet/smartphone to communicate with providers. Cancer survivors with a usual source of care (OR 6.23, 95% CI 1.66-23.39) or health care office visits in a year (ORs 7.55-8.25) were significantly more likely to use EHRs to communicate. Although it was not observed in cancer survivors, a lower education level was associated with lower OPPC among adults without a history of cancer during COVID-19. CONCLUSIONS: Our findings identified vulnerable subgroups of cancer survivors who were left behind in OPPC, which is increasingly becoming part of health care. These vulnerable subgroups of cancer survivors with lower OPPC should be helped through multidimensional interventions to prevent further inequities.

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 score0.261
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.160
GPT teacher head0.459
Teacher spread0.299 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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