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Record W4281689039 · doi:10.2196/37212

Characteristics of Users of the Cook for Your Life Website, an Online Nutrition Resource for Persons Affected by Cancer: Descriptive Study

2022· article· en· W4281689039 on OpenAlexvenueaboutno aff
Eileen Rillamas‐Sun, Liza Schattenkerk, Sofia Cobos, Katherine Ueland, Ann Ogden Gaffney, Heather Greenlee

Bibliographic record

VenueJMIR Cancer · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsMedicineCancerGerontologyDescriptive statisticsPublic healthFamily medicineDemographyNursingInternal medicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Accessible nutrition resources tailored to patients with cancer, caregivers of cancer survivors, and people interested in cancer prevention are limited. Cook for Your Life is a bilingual (ie, English and Spanish) website providing science-based, nutrition information for people affected by cancer. OBJECTIVE: The aim of this study was to describe the characteristics of Cook for Your Life website users. METHODS: In December 2020, Cook for Your Life website visitors at least 18 years old were invited to participate in an online English-language survey. A Spanish version was offered in April 2021. Demographic, health, and cooking characteristics were collected. Persons with a cancer history were asked about treatment and side effects. Data were analyzed through December 2021 on those completing over half of the survey. Three groups were compared: people with a history of cancer diagnosis, caregivers of cancer survivors, and the general public (ie, people without a cancer history). Website use data were also compared. RESULTS: Among English-language respondents, 3346 initiated the survey and 2665 (79.65%) completed over half of the questions. Of these, 54.82% (n=1461) had a cancer diagnosis, 8.26% (n=220) were caregivers, and 36.92% (n=984) were from the general public. English-language respondents were US residents (n=2054, 77.07%), with some from Europe (n=285, 10.69%) and Canada (n=170, 6.38%). Cancer survivors were most likely 55 years of age or older, female, non-Hispanic White, with incomes over US $100,000, and college educated. Caregivers and the general public were younger and more racially and geographically diverse. The most common cancer malignancies among English-language cancer survivors were breast (629/1394, 45.12%) and gastrointestinal (209/1394, 14.99%). For Spanish-language respondents, 942 initiated the survey; of these, 681 (72.3%) were analyzed. Of the 681 analyzed, 13.5% (n=92) were cancer survivors, 6.8% (n=46) were caregivers, and 79.7% (n=543) were from the general public. Spanish-language respondents were also more likely to be female and highly educated, but were younger, were from South or Latin America, and had incomes less than US $30,000. Among Spanish-language cancer survivors, breast cancer (27/81, 33%) and gastrointestinal cancer (15/81, 19%) were the most common diagnoses. Website use data on over 2.2 million users from December 2020 to December 2021 showed that 52.29% of traffic was in English and 43.44% was in Spanish. Compared to survey respondents, a higher proportion of website users were male, younger, and from South or Central America and Europe. CONCLUSIONS: Cook for Your Life website users were demographically, socioeconomically, and geographically diverse, especially English-language respondents without a cancer history and all Spanish-language respondents. Improvements on website user diversity and reach for all patients with cancer and research on effective strategies for using this digital platform to support cancer prevention, treatment, and survivorship will continue. TRIAL REGISTRATION: ClinicalTrials.gov NCT04200482; https://www.clinicaltrials.gov/ct2/show/NCT04200482.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.120
GPT teacher head0.468
Teacher spread0.347 · 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
Published2022
Admission routes2
Has abstractyes

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