Participant Engagement in a Transmedia Storytelling Web-Based App Intervention for Mental Health of Latina Women: Qualitative Analysis
Notice bibliographique
Résumé
BACKGROUND: Stigma, fear, and lack of knowledge regarding treatment options or where to get help create delays for Latina women in accessing needed mental health help. Story-based media interventions hold appeal for Latina women. Thus, we drew upon the Social Cognitive Theory by Bandura to create an evidence-based, transmedia storytelling web-based app for mental health called Catalina: Confronting My Emotions to connect Latina women to a curated set of mental health resources. Understanding how Latina women perceive various aspects of the web-based app will help design future expansions. OBJECTIVE: A previously published analysis led to the development of a category on how participants related to the lead character (Catalina) in the story line of the web-based app as a real person. However, the purpose of this analysis was to gain an understanding of participants' experiences with the extension of the dramatic story line of the web-based app beyond Catalina to a Latina nurse-therapist character named Veronica, who was featured prominently in the app's interactive content and bonus videos. METHODS: Qualitative analyses were conducted with interview data from a community-based sample of 28 English-speaking Latina women aged between 21 and 50 years who scored above the threshold for anxiety (Generalized Anxiety Disorder-7) and/or depression (Patient Health Questionnaire-9) but were not suicidal at screening. Data were collected 72 hours after participants engaged with our transmedia storytelling web-based app for mental health. Grounded theory methodology guided the analysis and interpretation of data that had been collected telephonically, recorded, and transcribed with identifiers removed. Analyses included initial and focused coding using process codes (gerund form of verbs in codes focused on action), informed by symbolic interactionism, and the development of categories with properties through constant comparison, memo writing, and the use of charts and diagrams. RESULTS: Our participants experienced a multiphase process that was most heavily related to Veronica, the Latina nurse-therapist character in our web-based app, who led them through a process to a place of action. We conceptualized this process as moving from passive viewer to active participant of a transmedia storytelling web-based app intervention. Overall, 3 new conceptual categories provided insight into women's experiences, including encountering a trustworthy nurse-therapist character, taking in messages that dispel old beliefs, and preparing when and how to take action. Each category has nuanced properties that reflect participants' experiences. CONCLUSIONS: Active engagement with our web-based app led our sample to successfully transition from the viewpoint of the observer to the viewpoint of the experiencer, moving from a passive position of watching to active engagement that involved imagining, thinking, reflecting, and acting. Careful development of dramatic material for health-related web-based apps using transmedia story extension and bonus videos needs to be based on input from the target group from the start of development through evaluation and testing.
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 | 0,013 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».