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Record W4292847474 · doi:10.1136/bmjopen-2022-064324

User engagement with technology-mediated self-guided interventions for addictions: scoping review protocol

2022· article· en· W4292847474 on OpenAlexaff
Brad W. Brazeau, David C. Hodgins

Bibliographic record

VenueBMJ Open · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineProtocol (science)Psychological interventionAddictionAlternative medicineMedical educationPsychiatryPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Technology-mediated self-guided interventions (TMSGIs) for addictive disorders represent promising adjuncts and alternatives to traditional treatment approaches (eg, face-to-face psychotherapy). However, meaningful evaluation of such interventions remains elusive given the lack of consistent terminology and application. Preliminary findings suggest that TMSGIs are useful but engagement remains modest for various reasons reported by users, including lack of personalisation. The aim of this review is to explore how TMSGIs have been defined and applied in addictions populations with an emphasis on technical and logistical features associated with greater user engagement. METHODS AND ANALYSIS: This scoping review protocol was developed in accordance with the Arksey and O'Malley framework. Articles from electronic databases (ie, PsycINFO, Embase, MEDLINE and CINAHL) will be included if they targeted adolescents or adults with one or more substance or behavioural addictions, excessive behaviours or aspects thereof (eg, cravings) using a privately accessible technology-mediated intervention. Two independent reviewers will screen titles and abstracts for relevance before commencing full-text reviews. Extracted data will be presented in descriptive, tabular and graphical summaries as appropriate. ETHICS AND DISSEMINATION: Ethics committee approval is not required for this study. Review findings will be used to guide the development of preliminary recommendations for real-time addiction intervention development and provision. Emphasis will be placed on practical considerations of user engagement, accessibility, usability and cost. Knowledge users, including clinicians, researchers and people with lived experience, will be engaged for development of one such intervention following publication of review findings. REGISTRATION: This scoping review was registered with the Open Science Framework on 15 April 2022 and can be located at http://www.osf.io/3utp9/.

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)0.109
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: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.109
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1090.093
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0110.011
Bibliometrics0.0160.014
Science and technology studies0.0050.006
Scholarly communication0.0090.008
Open science0.0060.007
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0940.021

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 head0.246
GPT teacher head0.572
Teacher spread0.327 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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