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Record W4312843902 · doi:10.2196/35601

Exploring Intergenerational Communication on Social Media Group Chats as a Cancer Prevention Intervention Opportunity Among Vietnamese American Families: Qualitative Study

2022· article· en· W4312843902 on OpenAlexvenueno aff
Huong T. Duong, Suellen Hopfer

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
FundersChao Family Comprehensive Cancer CenterNational Cancer InstituteUniversity of California, Irvine
KeywordsVietnameseConversationSocial mediaCancer preventionQualitative researchPsychologyContext (archaeology)Focus groupIntervention (counseling)MedicineCancerNursingWorld Wide WebCommunicationSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Families use social media group chats to connect with each other about daily life and to share information. Although cancer is not a frequent topic of conversation in family settings, the adoption of mobile technology in the family context presents a novel opportunity to promote cancer prevention information. To the best of our knowledge, few studies have used private social media group chats to promote cancer prevention information to family members. OBJECTIVE: In this formative study, we investigated how family group chat platforms can be leveraged to encourage colorectal cancer screening, human papillomavirus vaccination, and cervical cancer screening among intergenerational Vietnamese American families. This study aimed to cocreate a family-based communication intervention for introducing cancer screening information in family group chats. We sought to understand family members' motivations for using group chats, family dynamics and conversation patterns, and group chat experiences and cultural norms for interacting with family members. METHODS: Overall, 20 audio-recorded and semistructured interviews were conducted with young Vietnamese adults. The study was conducted between August and October 2018. Participants were Vietnamese Americans; aged between 18 and 44 years; living in Orange County, California; had an existing family group chat; and expressed an interest in becoming family health advocates. Data were analyzed using a framework analysis. RESULTS: In total, 13 (65%) of the 20 young adults reported having >1 group chat with their immediate and extended family. Preventive health was not a typical topic of family conversations, but food, family announcements, personal updates, humorous videos or photos, and current events were. Young adults expressed openness to initiating conversations with family members about cancer prevention; however, they also raised concerns that may influence family members' receptivity to the messages. Themes that could potentially impact family members' willingness to accept cancer prevention messages included family status and hierarchy, gender dynamics, relational closeness in the family, and source trust and credibility. These considerations may impact whether families will be open to receiving cancer screening information and acting on it. The participants also mentioned practical considerations for intervention and message design, which included the Vietnamese cultural conversation etiquette of hỏi thăm, respect for a physician's recommendation, prevention versus symptom orientation, the family health advocate's bilingual capacity, and the busy lives of family members. In response to exemplar messages, participants mentioned that they preferred to personalize template messages to accommodate conversational norms in their family group chats. CONCLUSIONS: The findings of this study inform the development of a social media intervention for increasing preventive cancer screening in Vietnamese American families.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.377
GPT teacher head0.530
Teacher spread0.154 · 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 designQualitative
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 routes1
Has abstractyes

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