MétaCan
Menu
Back to cohort
Record W2525794602 · doi:10.2196/mental.6001

Web-Based Intervention to Reduce Substance Abuse and Depressive Symptoms in Mexico: Development and Usability Test

2016· article· en· W2525794602 on OpenAlexvenueno aff
Marcela Tiburcio, Ma. Asunción Lara, Araceli Aguilar Abrego, Morise Fernández-Torres, Nora Angélica Martínez-Vélez, Alejandro Sánchez

Bibliographic record

VenueJMIR Mental Health · 2016
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityIntervention (counseling)Test (biology)Clinical psychologyWeb applicationPsychologySubstance abusePsychiatryMedicineWorld Wide WebComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: The development of Web-based interventions for substance abuse in Latin America is a new field of interest with great potential for expansion to other Spanish-speaking countries. OBJECTIVE: This paper describes a project aimed to develop and evaluate the usability of the Web-based Help Program for Drug Abuse and Depression (Programa de Ayuda para Abuso de Drogas y Depresión, PAADD, in Spanish) and also to construct a systematic frame of reference for the development of future Web-based programs. METHODS: The PAADD aims to reduce substance use and depressive symptoms with cognitive behavioral techniques translated into Web applications, aided by the participation of a counselor to provide support and guidance. This Web-based intervention includes 4 steps: (1) My Starting Point, (2) Where Do I Want to Be? (3) Strategies for Change, and (4) Maintaining Change. The development of the program was an interactive multistage process. The first stage defined the core structure and contents, which were validated in stage 2 by a group of 8 experts in addiction treatment. Programming of the applications took place in stage 3, taking into account 3 types of end users: administrators, counselors, and substance users. Stage 4 consisted of functionality testing. In stage 5, a total of 9 health professionals and 20 drug users currently in treatment voluntarily interacted with the program in a usability test, providing feedback about adjustments needed to improve users' experience. RESULTS: The main finding of stage 2 was the consensus of the health professionals about the cognitive behavioral strategies and techniques included in PAADD being appropriate for changing substance use behaviors. In stage 5, the health professionals found the functionalities easy to learn; their suggestions were related to the page layout, inclusion of confirmation messages at the end of activities, avoiding "read more" links, and providing feedback about every activity. On the other hand, the users said the information presented within the modules was easy to follow and suggested more dynamic features with concrete instructions and feedback. CONCLUSIONS: The resulting Web-based program may have advantages over traditional face-to-face therapies owing to its low cost, wide accessibility, anonymity, and independence of time and distance factors. The detailed description of the process of designing a Web-based program is an important contribution to others interested in this field. The potential benefits must be verified in specific studies. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 25429892; http://www.controlled-trials.com/ISRCTN25429892 (Archived by WebCite at http://www.webcitation.org/6ko1Fsvym).

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.384
Teacher spread0.358 · 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 designNon-randomized trial
Domainnot available
GenreEmpirical

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

Citations30
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Mental HealthSame topicDigital Mental Health InterventionsFrench-language works237,207