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Record W2042424129 · doi:10.2196/jmir.3147

Web-Based Intervention Programs for Depression: A Scoping Review and Evaluation

2014· review· en· W2042424129 on OpenAlexaffabout
Tian Renton, Herman Tang, Naomi Ennis, Michael D. Cusimano, Shree Bhalerao, Tom A. Schweizer, Jane Topolovec‐Vranic

Bibliographic record

VenueJournal of Medical Internet Research · 2014
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsUsabilityPsychological interventionRandomized controlled trialMedicineIntervention (counseling)Health careMedical educationWorld Wide WebPsychologyInternet privacyNursingComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Although depression is known to affect millions of people worldwide, individuals seeking aid from qualified health care professionals are faced with a number of barriers to treatment including a lack of treatment resources, limited number of qualified service providers, stigma associated with diagnosis and treatment, prolonged wait times, cost, and barriers to accessibility such as transportation and clinic locations. The delivery of depression interventions through the Internet may provide a practical solution to addressing some of these barriers. OBJECTIVE: The purpose of this scoping review was to answer the following questions: (1) What Web-delivered programs are currently available that offer an interactive treatment component for depression?, (2) What are the contents, accessibility, and usability of each identified program?, and (3) What tools, supports, and research evidence are available for each identified program? METHODS: Using the popular search engines Google, Yahoo, and Bing (Canadian platforms), two reviewers independently searched for interactive Web-based interventions targeting the treatment of depression. The Beacon website, an information portal for online health applications, was also consulted. For each identified program, accessibility, usability, tools, support, and research evidence were evaluated and programs were categorized as evidence-based versus non-evidence-based if they had been the subject of at least one randomized controlled trial. Programs were scored using a 28-point rating system, and evidence- versus non-evidence-based programs were compared and contrasted. Although this review included all programs meeting exclusion and inclusion criteria found using the described search method, only English language Web-delivered depression programs were awarded an evaluation score. RESULTS: The review identified 32 programs meeting inclusion criteria. There was a great deal of variability among the programs captured in this evaluation. Many of the programs were developed for general adolescent or adult audiences, with few (n=2) focusing on special populations (eg, military personnel, older adults). Cognitive behavioral therapy was the most common therapeutic approach used in the programs described. Program interactive components included mood assessments and supplementary homework sheets such as activity planning and goal setting. Only 12 of the programs had published evidence in support of their efficacy and treatment of depressive symptoms. CONCLUSIONS: There are a number of interactive depression interventions available through the Internet. Recommendations for future programs, or the adaptation of existing programs include offering a greater selection of alternative languages, removing registration restrictions, free trial periods for programs requiring user fees, and amending programs to meet the needs of special populations (eg, those with cognitive and/or visual impairments). Furthermore, discussion of specific and relevant topics to the target audience while also enhancing overall user control would contribute to a more accessible intervention tool.

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)0.028
metaresearch head score (Gemma)0.074
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 score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.074
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.010
Bibliometrics0.0160.016
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.400
GPT teacher head0.656
Teacher spread0.256 · 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

Citations127
Published2014
Admission routes2
Has abstractyes

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