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Enregistrement 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 sur OpenAlexvenueaboutno aff
Eileen Rillamas‐Sun, Liza Schattenkerk, Sofia Cobos, Katherine Ueland, Ann Ogden Gaffney, Heather Greenlee

Notice bibliographique

RevueJMIR Cancer · 2022
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Literacy and Information Accessibility
Établissements canadiensnon disponible
Organismes subventionnairesNational Cancer Institute
Mots-clésMedicineCancerGerontologyDescriptive statisticsPublic healthFamily medicineDemographyNursingInternal medicineSociology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,143
Score d'incertitude au seuil0,729

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,120
Tête enseignante GPT0,468
Écart entre enseignants0,347 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2022
Routes d'admission2
Résumé présentoui

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