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Record W4225109001 · doi:10.1145/3491102.3502046

Meeting Users Where They Are: User-centered Design of an Automated Text Messaging Tool to Support the Mental Health of Young Adults

2022· article· en· W4225109001 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCHI Conference on Human Factors in Computing Systems · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Toronto
FundersNational Center for Advancing Translational SciencesNational Institute of Mental Health
KeywordsMental healthMoodAnxietyApplied psychologyPsychologyComputer scienceDigital healthMedical educationClinical psychologyHealth careMedicinePsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Young adults have high rates of mental health conditions, but most do not want or cannot access formal treatment. We therefore recruited young adults with depression or anxiety symptoms to co-design a digital tool for self-managing their mental health concerns. Through study activities-consisting of an online discussion group and a series of design workshops-participants highlighted the importance of easy-to-use digital tools that allow them to exercise independence in their self-management. They described ways that an automated messaging tool might benefit them by: facilitating experimentation with diverse concepts and experiences; allowing variable depth of engagement based on preferences, availability, and mood; and collecting feedback to personalize the tool. While participants wanted to feel supported by an automated tool, they cautioned against incorporating an overtly human-like motivational tone. We discuss ways to apply these findings to improve the design and dissemination of digital mental health tools for young adults.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.103
GPT teacher head0.399
Teacher spread0.295 · 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