Abstract 5905: User characteristics of “Cook For Your Life” - a website designed to support cancer patients and survivors
Notice bibliographique
Résumé
Abstract Purpose: Cook for Your Life (cookforyourlife.org) is a bilingual, science-based nutrition and culinary website designed for cancer patients and survivors based at the Fred Hutchinson Cancer Research Center. The website has been used as a tool and resource for health intervention research. This analysis described the characteristics of English and Spanish language users who responded to an online survey. Methods: Visitors to cookforyourlife.org were invited to participate in an online survey collecting demographic characteristics and health behaviors. Those at least 18 years old were eligible. Respondents with a cancer diagnosis were asked a subset of questions about treatment and side effects. English language (EL) and Spanish language (SL) versions launched in December 2020 and April 2021, respectively. Survey data were analyzed through October 2021 and only included those completing at least 50% of the survey questions. Demographic characteristics from web analytics data were compared. Results: Among EL respondents, 3039 initiated the survey and 2417 completed at least 50% of the questions. Of these, 53% were persons with a cancer diagnosis, 8% were caregivers of cancer patients, and 39% other. The majority of EL respondents were US residents (77%), but many were also from Europe (11%) and Canada (6%). Cancer patients/survivors were most likely to be ≥55 years old, female, non-Hispanic white, have income >$100K, and be college educated. Caregivers and others were younger, but otherwise had similar demographics. Among cancer patients/survivors, 46% had breast cancer and 7% pancreatic and 49% reported having treatment side effects in the past week, with 31% citing fatigue and 15% anxiety. Among SL respondents, 804 initiated the survey and 545 were eligible for analysis. Of these, 17% were cancer patients/survivors, 8% caregivers, and 75% other. SL respondents were also more likely to be female and highly educated, but were younger, from South/Latin America, and had income <$30K. Among SL cancer survivors, 31% had breast cancer and 8% had colorectal. Web analytics data on 1.5+ million visitors from December 2020 to October 2021 indicated most visitors were 71% female and lived in South/Latin America (35%) or the US (31%). Conclusions: Respondents of the Cook for Your Life English-language website survey were predominantly US women with high socioeconomic status; many had history of breast cancer. Conversely, Spanish-language respondents had more socioeconomic diversity, but fewer were diagnosed with cancer. Web analytics data suggested survey respondents may differ demographically from general website users. Knowledge about our website users is necessary for developing targeted strategies to improve reach. Future research efforts will focus on delivering content to more varied populations of cancer patients and survivors, their caregivers, and individuals interested in cancer prevention. Citation Format: Eileen Rillamas-Sun, Liza Schattenkerk, Sofia Cobos, Kate Ueland, Heather Greenlee. User characteristics of “Cook For Your Life” - a website designed to support cancer patients and survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5905.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».