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Record W2419371092 · doi:10.2196/mental.5349

Finding Web-Based Anxiety Interventions on the World Wide Web: A Scoping Review

2016· review· en· W2419371092 on OpenAlexvenueno aff
Miriam T. Ashford, Ellinor K. Olander, Susan Ayers

Bibliographic record

VenueJMIR Mental Health · 2016
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyPsychological interventionIntervention (counseling)World Wide WebWeb applicationComputer scienceMedicineInternet privacyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: One relatively new and increasingly popular approach of increasing access to treatment is Web-based intervention programs. The advantage of Web-based approaches is the accessibility, affordability, and anonymity of potentially evidence-based treatment. Despite much research evidence on the effectiveness of Web-based interventions for anxiety found in the literature, little is known about what is publically available for potential consumers on the Web. OBJECTIVE: Our aim was to explore what a consumer searching the Web for Web-based intervention options for anxiety-related issues might find. The objectives were to identify currently publically available Web-based intervention programs for anxiety and to synthesize and review these in terms of (1) website characteristics such as credibility and accessibility; (2) intervention program characteristics such as intervention focus, design, and presentation modes; (3) therapeutic elements employed; and (4) published evidence of efficacy. METHODS: Web keyword searches were carried out on three major search engines (Google, Bing, and Yahoo-UK platforms). For each search, the first 25 hyperlinks were screened for eligible programs. Included were programs that were designed for anxiety symptoms, currently publically accessible on the Web, had an online component, a structured treatment plan, and were available in English. Data were extracted for website characteristics, program characteristics, therapeutic characteristics, as well as empirical evidence. Programs were also evaluated using a 16-point rating tool. RESULTS: The search resulted in 34 programs that were eligible for review. A wide variety of programs for anxiety, including specific anxiety disorders, and anxiety in combination with stress, depression, or anger were identified and based predominantly on cognitive behavioral therapy techniques. The majority of websites were rated as credible, secure, and free of advertisement. The majority required users to register and/or to pay a program access fee. Half of the programs offered some form of paid therapist or professional support. Programs varied in treatment length and number of modules and employed a variety of presentation modes. Relatively few programs had published research evidence of the intervention's efficacy. CONCLUSIONS: This review represents a snapshot of available Web-based intervention programs for anxiety that could be found by consumers in March 2015. The consumer is confronted with a diversity of programs, which makes it difficult to identify an appropriate program. Limited reports and existence of empirical evidence for efficacy make it even more challenging to identify credible and reliable programs. This highlights the need for consistent guidelines and standards on developing, providing, and evaluating Web-based interventions and platforms with reliable up-to-date information for professionals and consumers about the characteristics, quality, and accessibility of Web-based interventions.

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)0.023
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.093
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0370.031
Science and technology studies0.0020.002
Scholarly communication0.0070.007
Open science0.0030.005
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.160
GPT teacher head0.529
Teacher spread0.368 · 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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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