Privacy Concerns Versus Personalized Health Content—Pregnant Individuals’ Willingness to Share Personal Health Information on Social Media: Survey Study
Notice bibliographique
Résumé
Background: Often lacking immediate access to care providers, pregnant individuals frequently turn to web-based sources for information to address their evolving physical and mental health needs. Social media has gained increasing prominence as a source of news and information despite privacy concerns and unique risks posed to the pregnant population. objectives: This study investigated the extent to which patients may be willing to disclose personal health information to social media companies in exchange for more personalized health content. Methods: We designed and deployed an electronic survey to pregnant individuals worldwide electronically in 2023. We used the classical Internet Users' Information Privacy Concerns (IUIPC) model to examine how privacy concerns modulate pregnant individuals' behaviors and beliefs regarding risk and trust when using social media for health purposes. Results were analyzed using partial least squares structural equation modeling. Results: Among 317 respondents who initiated the survey, 84% (265/317) of the respondents remained in the study, providing complete responses. Among them, 54.7% (145/265) indicated willingness to provide their personalized health information for receiving personalized health content via social media, while 26% (69/265) were uncertain and 19.3% (51/265) were opposed. Our estimated IUIPC model results are statistically significant and qualitatively align with the classic IUIPC model for the general population, which was previously found in an e-commerce context. The structural model revealed that privacy concerns (IUIPC) negatively affected trusting beliefs (β=-0.408; P<.001) and positively influenced risk beliefs (β=0.442; P<.001). Trusting beliefs negatively impacted risk beliefs (β=-o.362; P<.001) and positively affected the intention to disclose personal health information (β=o.266; P<.001). Risk beliefs negatively influenced the intention to disclose (β=-0.281; P<.001). The model explained 41.5% of the variance in the intention to disclose personal health information (R²=0.415). In parallel with pregnant individuals' willingness to share, we find that they have heightened privacy concerns and their use of social media for information seeking is largely impacted by their trust in the platforms. This heightened concern significantly affects both their trusting beliefs, making them less inclined to trust social media companies, and their risk beliefs, leading them to perceive greater risks in sharing personal health information. However, within this population, an increase in trust toward social media companies leads to a more substantial decrease in perceived risks than what has been previously observed in the general population. Conclusions: We find that more than half of the pregnant individuals are open to sharing their personal health information to receive personalized content about health via social media, although they have more privacy concerns than the general population. This study emphasizes the need for policy regarding the protection of health data on social media for the pregnant population and beyond.
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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 | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».