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Record W4210868923 · doi:10.2196/31018

Web-Based Interventions to Help Australian Adults Address Depression, Anxiety, Suicidal Ideation, and General Mental Well-being: Scoping Review

2022· article· en· W4210868923 on OpenAlexvenueno aff
Gemma Skaczkowski, Shannen van der Kruk, Sophie Loxton, Donna Hughes, Cate Howell, Deborah Turnbull, Neil Jensen, Matthew Smout, Kate M. Gunn

Bibliographic record

VenueJMIR Mental Health · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersRACGP FoundationRoyal Australian College of General Practitioners
KeywordsMental healthPsychological interventionSuicidal ideationAnxietyThe InternetWeb applicationPsychologyMedicinePsychiatryInternet privacySuicide preventionPoison controlWorld Wide WebComputer scienceMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: A large number of Australians experience mental health challenges at some point in their lives. However, in many parts of Australia, the wait times to see general practitioners and mental health professionals can be lengthy. With increasing internet use across Australia, web-based interventions may help increase access to timely mental health care. As a result, this is an area of increasing research interest, and the number of publicly available web-based interventions is growing. However, it can be confusing for clinicians and consumers to know the resources that are evidence-based and best meet their needs. OBJECTIVE: This study aims to scope out the range of web-based mental health interventions that address depression, anxiety, suicidal ideation, or general mental well-being and are freely available to Australian adults, along with their impact, acceptability, therapeutic approach, and key features. METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews (PRISMA-ScR [PRISMA extension for Scoping Reviews]) guided the review process. Keywords for the search were depression, anxiety, suicide, and well-being. The search was conducted using Google as well as the key intervention databases Beacon, Head to Health, and e-Mental Health in Practice. Interventions were deemed eligible if they targeted depression, anxiety, suicidal ideation, or general mental well-being (eg, resilience) in adults; and were web-based, written in English, interactive, free, and publicly available. They also had to be guided by an evidence-based therapeutic approach. RESULTS: Overall, 52 eligible programs were identified, of which 9 (17%) addressed depression, 15 (29%) addressed anxiety, 13 (25%) addressed general mental well-being, and 13 (25%) addressed multiple issues. Only 4% (2/52) addressed distress in the form of suicidal ideation. The most common therapeutic approach was cognitive behavioral therapy. Half of the programs guided users through exercises in a set sequence, and most programs enabled users to log in and complete the activities on their own without professional support. Just over half of the programs had been evaluated for their effectiveness in reducing symptoms, and 11% (6/52) were being evaluated at the time of writing. Program evaluation scores ranged from 44% to 100%, with a total average score of 85%. CONCLUSIONS: There are numerous web-based programs for depression, anxiety, suicidal ideation, and general well-being, which are freely and publicly available in Australia. However, identified gaps include a lack of available web-based interventions for culturally and linguistically diverse populations and programs that use newer therapeutic approaches such as acceptance and commitment therapy and dialectical behavior therapy. Despite most programs included in this review being of good quality, clinicians and consumers should pay careful attention when selecting which program to recommend and use, as variations in the levels of acceptability and impact of publicly available programs do exist.

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)0.091
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 score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.091
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.009
Bibliometrics0.0120.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.041
GPT teacher head0.435
Teacher spread0.395 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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