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Record W2766777328 · doi:10.2196/mental.8571

A Transmedia Storytelling Intervention With Interactive Elements to Benefit Latinas’ Mental Health: Feasibility, Acceptability, and Efficacy

2017· article· en· W2766777328 on OpenAlexvenueno aff
MarySue V. Heilemann, Patricia D. Soderlund, Priscilla Kehoe, Mary‐Lynn Brecht

Bibliographic record

VenueJMIR Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Institute of Nursing ResearchNational Institutes of Health
KeywordsMental healthAnxietyPsychological interventionIntervention (counseling)PsychologyPatient Health QuestionnaireMedicineClinical psychologyPsychiatryDepressive symptoms

Abstract

fetched live from OpenAlex

BACKGROUND: Latinos report higher rates of depression and anxiety than US whites but are less likely to receive care. Transmedia storytelling interventions accessible on the Internet via smartphones, tablets, and computers hold promise for reducing reluctance to explore or get help for symptoms because they are private, convenient, and can reach large numbers of people, including Latinas with mental health needs. OBJECTIVE: The purpose of this study was to examine the feasibility, acceptability, and preliminary efficacy of a mental health transmedia intervention for Latinas with elevated symptoms of depression, anxiety, or both. METHODS: A total of 28 symptomatic English-speaking Latina women aged 21 to 48 years participated in a 6-week study using a within-group design. All aspects of the study were completed via telephone or Internet. Participants used their personal devices to engage the Web-based transmedia intervention (in English) that included story-based videos, a data-informed psychotherapeutic video, an interactive video sequence, and a blog written from the point of view of one of the characters with links to mental health resources. Perceived confidence to get help and perceived importance for seeking immediate help were both measured using single-item questions. Participants completed surveys at baseline (via telephone) and 1 and 6 weeks after media engagement that measured various factors, including depression (Patient Health Questionnaire; PHQ-9 and PHQ-8) and anxiety (Generalized Anxiety Disorder scale; GAD-7). A telephone interview was conducted within 72 hours of media engagement. Action taken or intentions to get help (single-item question) and talking about the videos with others (single-item question) were measured 1 and 6 weeks after media engagement. Repeated measures analysis of variance was used to assess change in depression (PHQ-8) and anxiety (GAD-7) before transmedia engagement and 1 and 6 weeks after. Spearman correlations evaluated the association of confidence and importance of getting help with action taken, anxiety, and depression. RESULTS: =18.7, P<.001) significantly reduced across time. Higher levels of confidence were significantly associated with actions taken at 1 (P=.005) and 6 weeks (P=.04), and higher levels of importance were significantly associated with actions taken at 1 (P=.009) and 6 weeks (P=.003). Higher levels of confidence were associated with lower levels of depression (P=.04) and anxiety (P=.01) at 6 weeks. CONCLUSIONS: Preliminary findings indicate a culturally tailored mental health transmedia intervention is a feasible approach that holds promise for engaging large numbers of symptomatic English-speaking Latina women to begin the process of seeking help, as well as decreasing symptoms of anxiety and depression.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.057
GPT teacher head0.456
Teacher spread0.399 · 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 designNon-randomized trial
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

Citations21
Published2017
Admission routes1
Has abstractyes

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