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Record W2803107171 · doi:10.2196/mental.9655

eMental Healthcare Technologies for Anxiety and Depression in Childhood and Adolescence: Systematic Review of Studies Reporting Implementation Outcomes

2018· review· en· W2803107171 on OpenAlexaffvenue
Lori Wozney, Patrick J. McGrath, Nicole D. Gehring, Kathryn Bennett, Anna Huguet, Lisa Hartling, Michele P. Dyson, Amir Soleimani, Amanda S. Newton

Bibliographic record

VenueJMIR Mental Health · 2018
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsMcMaster UniversityImpactUniversity of AlbertaDalhousie UniversityIzaak Walton Killam Health Centre
Fundersnot available
KeywordsAnxietyHealth careDepression (economics)FidelityMedicineHealth technologyClinical psychologyPsychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Anxiety disorders and depression are frequent conditions in childhood and adolescence. eMental healthcare technologies may improve access to services, but their uptake within health systems is limited. OBJECTIVE: The objective of this review was to examine and describe how the implementation of eMental healthcare technologies for anxiety disorders and depression in children and adolescents has been studied. METHODS: We conducted a search of 5 electronic databases and gray literature. Eligible studies were those that assessed an eMental healthcare technology for treating or preventing anxiety or depression, included children or adolescents (<18 years), or their parents or healthcare providers and reported findings on technology implementation. The methodological quality of studies was evaluated using the Mixed Methods Appraisal Tool. Outcomes of interest were based on 8 implementation outcomes: acceptability (satisfaction with a technology), adoption (technology uptake and utilization), appropriateness ("fitness for purpose"), cost (financial impact of technology implementation), feasibility (extent to which a technology was successfully used), fidelity (implementation as intended), penetration ("spread" or "reach" of the technology), and sustainability (maintenance or integration of a technology within a healthcare service). For extracted implementation outcome data, we coded favorable ratings on measurement scales as "positive results" and unfavorable ratings on measurement scales as "negative results." Those studies that reported both positive and negative findings were coded as having "mixed results." RESULTS: A total of 46 studies met the inclusion criteria, the majority of which were rated as very good to excellent in methodological quality. These studies investigated eMental healthcare technologies for anxiety (n=23), depression (n=18), or both anxiety and depression (n=5). Studies of technologies for anxiety evaluated the following: (1) acceptability (78%) reported high levels of satisfaction, (2) adoption (43%) commonly reported positive results, and (3) feasibility (43%) reported mixed results. Studies of technologies for depression evaluated the following: (1) appropriateness (56%) reported moderate helpfulness and (2) acceptability (50%) described a mix of both positive and negative findings. Studies of technologies designed to aid anxiety and depression commonly reported mixed experiences with acceptability and adoption and positive findings for appropriateness of the technologies for treatment. Across all studies, cost, fidelity, and penetration and sustainability were the least measured implementation outcomes. CONCLUSIONS: Acceptability of eMental healthcare technology is high among users and is the most commonly investigated implementation outcome. Perceptions of the appropriateness and adoption of eMental healthcare technology were varied. Implementation research that identifies, evaluates, and reports on costs, sustainability, and fidelity to clinical guidelines is crucial for making high-quality eMental healthcare available to children and adolescents.

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)0.002
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.291
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
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 head0.117
GPT teacher head0.557
Teacher spread0.439 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations60
Published2018
Admission routes2
Has abstractyes

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