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Record W3037075243 · doi:10.1016/j.eclinm.2020.100442

A comparison of electronically-delivered and face to face cognitive behavioural therapies in depressive disorders: A systematic review and meta-analysis

2020· review· en· W3037075243 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEClinicalMedicine · 2020
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsPrograms for Assessment of Technology in Health Research InstituteImpactUniversity of OttawaMcMaster University
FundersCanadian Institutes of Health Research
KeywordsCINAHLMedicineMEDLINESystematic reviewData extractionRandomized controlled trialPsycINFOMeta-analysisCognitionFace-to-faceInclusion (mineral)Health careClinical psychologyPsychiatryPsychological interventionPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive behavioural therapy (CBT) is a widely used treatment for depression. However, limited resource availability poses several barriers to patients seeking access to care, including lengthy wait times and geographical limitations. This has prompted health care services to introduce electronically delivered CBT (eCBT) to facilitate access. Although previous reviews have compared the effects of eCBT to face-to-face CBT, there is an overall lack of adequately powered and up-to-date evidence in the literature to provide a reliable comparison between the two modes of administration. The purpose of this study is to evaluate the effects of eCBT compared to face-to-face CBT through a systematic review of the literature. METHODS: To be eligible for this review, studies needed to be randomized controlled trials evaluating the clinical effectiveness of any form of eCBT compared to face-to-face CBT. These encompassed studies evaluating a wide range of outcomes including severity of symptoms, adverse outcomes, clinically relevant outcomes, global functionality, participant satisfaction, quality of life, and affordability. There were no restrictions on participant age or sex.We searched MEDLINE, EMBASE, Psych Info, Cochrane CENTRAL and CINAHL databases from inception to February 20th, 2020 using a comprehensive search strategy. All stages of literature screening and data extraction were completed independently in duplicate. Data extraction and risk of bias analyses, including GRADE ratings, were conducted on studies meeting inclusion criteria. Qualitative measures are reported in a narrative summary. We pooled quantitative data in meta-analyses to provide an estimated summary effect. This review adheres to PRISMA reporting guidelines. FINDINGS: In total, we included 17 studies in our analyses. Our results demonstrated that eCBT was more effective than face-to-face CBT at reducing depression symptom severity (Standardized mean difference [SMD]: -1.73; 95% confidence interval [CI]: -2.72, -0.74; GRADE: moderate quality of evidence). There were no significant differences between the two interventions on participant satisfaction (SMD 0.13 95%; CI -0.32, 0.59; GRADE: low quality of evidence). One RCT reported eCBT to be less costly than face-to-face CBT (GRADE: low quality of evidence). Results did not differ when stratified by subgroups such as participant age and study location. INTERPRETATION: Although we found eCBT to have moderate evidence of effectiveness in reducing symptoms of depression, high heterogeneity among studies precludes definitive conclusions for all outcomes. With the current reliance and accessibility of technology to increasing number of people worldwide, serious consideration in utilizing technology should be given to maximize accessibility for depression treatments. Our results found eCBT is at least as effective as face to face CBT, thus eCBT should be offered if preferred by patients and therapists. FUNDING: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.719
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0130.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.252
GPT teacher head0.547
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