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Record W4404414509 · doi:10.2196/58482

Exploring Pregnancy-Related Information-Sharing Behavior Among First-Time Southeast Asian Fathers: Qualitative Semistructured Interview Study

2024· article· en· W4404414509 on OpenAlexvenueno aff
Kidung Ageng, Anushia Inthiran

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2024
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintQualitative researchPsychologySociologyComputer scienceWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: While the benefits of fathers' engagement in pregnancy are well researched, little is known about first-time expectant fathers' information-seeking practices in Southeast Asia regarding pregnancy. In addition, there is a notable gap in understanding their information-sharing behaviors during the pregnancy journey. This information is important, as cultural norms are prevalent in Southeast Asia, and this might influence their information-sharing behavior, particularly about pregnancy. OBJECTIVE: This study aims to explore and analyze the pregnancy-related information-sharing behavior of first-time expectant fathers in Southeast Asia. This study specifically aims to investigate whether first-time fathers share pregnancy information, with whom they share it, through what means, and the reasons behind the decisions to share the information or not. METHODS: We conducted semistructured interviews with first-time Southeast Asian fathers in Indonesia, a sample country in the Southeast Asian region. We analyzed the data using quantitative descriptive analysis and qualitative content theme analysis. A total of 40 first-time expectant fathers were interviewed. RESULTS: The results revealed that 90% (36/40) of the participants shared pregnancy-related information with others. However, within this group, more than half (22/40, 55%) of the participants shared the information exclusively with their partners. Only a small proportion, 10% (4/40), did not share any information at all. Among those who did share, the most popular approach was face-to-face communication (36/40, 90%), followed by online messaging apps (26/40, 65%). The most popular reason for sharing was to validate information (14/40, 35%), while the most frequent reason for not sharing with anyone beyond their partner was because of the preference for asking for information rather than sharing (12/40, 30%). CONCLUSIONS: This study provides valuable insights into the pregnancy-related information-sharing behaviors of first-time fathers in Southeast Asia. It enhances our understanding of how first-time fathers share pregnancy-related information and how local cultural norms and traditions influence these practices. In contrast to first-time fathers in high-income countries, the information-sharing behavior of first-time Southeast Asian fathers is defined by cultural nuances. Culture plays a crucial role in their daily decision-making processes. Therefore, this emphasizes the importance of cultural considerations in future discussions and the development of intervention programs related to pregnancy for first-time Southeast Asian fathers. In addition, this study sheds light on the interaction processes that first-time fathers engage in with others, highlighting areas where intervention programs may be necessary to improve their involvement during pregnancy. For example, first-time fathers actively exchange new information found with their partners; therefore, creating features or platforms that facilitate this process could improve their overall experience. Furthermore, health practitioners should take a more proactive approach in engaging with first-time fathers, as currently there is a communication gap between them.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.339
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes1
Has abstractyes

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