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Record W2999118048 · doi:10.2196/15801

The Acceptability of Text Messaging to Help African American Women Manage Anxiety and Depression: Cross-Sectional Survey Study

2020· article· en· W2999118048 on OpenAlexvenueno aff
Terika McCall, Todd A. Schwartz, Saif Khairat

Bibliographic record

VenueJMIR Mental Health · 2020
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersU.S. National Library of Medicine
KeywordsAnxietyMental healthPsychological interventionMedicineDepression (economics)PopulationMobile phonePhoneFamily medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The rates of mental illness among African American women are comparable with the general population; however, they significantly underutilize mental health services compared with their white counterparts. Previous studies revealed that interventions delivered via text messaging are effective and can be used to increase access to services and resources. More insight into whether or not this modality is acceptable for use to deliver mental health care to help African American women manage anxiety and depression is needed. OBJECTIVE: This exploratory study aimed to gain insight into the acceptability of using text messaging to help African American women manage anxiety and depression. METHODS: A self-administered Web-based survey was launched in June 2018 and closed in August 2018. Eligible participants were African American women (18 years or older) who reside in the United States. Participants were recruited through convenience sampling (eg, email sent via listservs and social media posts). Respondents were provided an anonymous link to the questionnaire. The survey consisted of 53 questions on the following subjects: sociodemographic characteristics, attitudes toward seeking professional psychological help, mobile phone use, and acceptability of using a mobile phone to receive mental health care. RESULTS: The results of this exploratory study (N=101) showed that fewer than half of respondents endorsed the use of text messaging to communicate with a professional to receive help to manage anxiety (49/101, 48.5%) and depression (43/101, 42.6%). Approximately 51.4% (52/101) agreed that having the option to use text messaging to communicate with a professional if they are dealing with anxiety would be helpful. Similarly, 48.5% (49/101) agreed that having the option to use text messaging to communicate with a professional if they are dealing with depression would be helpful. Among participants who agreed that text messaging would be helpful, more than 80% noted being comfortable with its use to receive help for managing anxiety (approximately 86%, 45/52) and depression (approximately 82%, 40/49; highly significant positive association, all P<.001). More than 50% of respondents (56/101, 55.4%) indicated having concerns about using text messaging. No statistically significant associations were found between age and agreement with the use of text messaging to communicate with a professional to receive help for managing anxiety (P=.26) or depression (P=.27). CONCLUSIONS: The use of text messaging was not highly endorsed by African American women as an acceptable mode of communication with a professional to help them manage anxiety or depression. Concerns around privacy, confidentiality, and the impersonal feel of communicating about sensitive issues via text messages must be addressed for this modality to be a viable option. The findings of this study demonstrated the need for further research into the use of mobile technology to provide this population with more accessible and convenient options for mental health care.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.434
Teacher spread0.387 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2020
Admission routes1
Has abstractyes

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