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Record W2114131466 · doi:10.2196/resprot.4358

Depression Awareness and Self-Management Through the Internet: Protocol for an Internationally Standardized Approach

2015· article· en· W2114131466 on OpenAlexfundvenueno aff
Ella Arensman, Nicole Koburger, Celine Larkin, Gillian Karwig, Claire Coffey, Margaret Maxwell, Fiona Harris, Christine Rummel‐Kluge, Merike Sisask, Anna Alexandrova‐Karamanova, Víctor Pérez, György Purebl, A. Cebrià, Diego Palao, Susana Costa, Lauraliisa Mark, Mónika Ditta Tóth, Marieta Gecheva, Angela Ibelshäuser, Ricardo Gusmão, Ulrich Hegerl

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersVrije Universiteit AmsterdamUniversity of TasmaniaDalhousie UniversityEuropean CommissionU.S. Department of Health and Human Services
KeywordsProtocol (science)The InternetMedicineMental healthDepression (economics)Health careCognitionCognitive behavioral therapyEuropean unionManagement of depressionPsychologyNursingMedical educationPsychiatryBusinessFamily medicineComputer scienceAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Depression incurs significant morbidity and confers increased risk of suicide. Many individuals experiencing depression remain untreated due to systemic and personal barriers to care. Guided Internet-based psychotherapeutic programs represent a promising means of overcoming such barriers and increasing the capacity for self-management of depression. However, existing programs tend to be available only in English and can be expensive to access. Furthermore, despite evidence of the effectiveness of a number of Internet-based programs, there is limited evidence regarding both the acceptability of such programs and feasibility of their use, for users and health care professionals. OBJECTIVE: This paper will present the protocol for the development, implementation, and evaluation of the iFightDepression tool, an Internet-based self-management tool. This is a cost-free, multilingual, guided, self-management program for mild to moderate depression cases. METHODS: The Preventing Depression and Improving Awareness through Networking in the European Union consortium undertook a comprehensive systematic review of the available evidence regarding computerized cognitive behavior therapy in addition to a consensus process involving mental health experts and service users to inform the development of the iFightDepression tool. The tool was implemented and evaluated for acceptability and feasibility of its use in a pilot phase in 5 European regions, with recruitment of users occurring through general practitioners and health care professionals who participated in a standardized training program. RESULTS: Targeting mild to moderate depression, the iFightDepression tool is based on cognitive behavioral therapy and addresses behavioral activation (monitoring and planning daily activities), cognitive restructuring (identifying and challenging unhelpful thoughts), sleep regulation, mood monitoring, and healthy lifestyle habits. There is also a tailored version of the tool for young people, incorporating less formal language and additional age-appropriate modules on relationships and social anxiety. The tool is accompanied by a 3-hour training intervention for health care professionals. CONCLUSIONS: It is intended that the iFightDepression tool and associated training for health care professionals will represent a valuable resource for the management of depression that will complement existing resources for health care professionals. It is also intended that the iFightDepression tool and training will represent an additional resource within a multifaceted approach to improving the care of depression and preventing suicidal behavior in Europe.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.562
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.427
GPT teacher head0.638
Teacher spread0.212 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations34
Published2015
Admission routes2
Has abstractyes

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