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Record W4205408972 · doi:10.2196/29566

Satisfaction and Acceptability Ratings of a Web-Based Self-help Intervention for Depression: Retrospective Cross-sectional Study From a Resource-Limited Country

2021· article· en· W4205408972 on OpenAlexvenueno aff
Ma. Asunción Lara, Pamela Patiño, Marcela Tiburcio, Laura Navarrete

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMedicineCross-sectional studyDepression (economics)Logistic regressionOdds ratioUsabilityDemographyFamily medicinePsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Web-based interventions are at an early stage in non-English-speaking low- and middle-income countries, where they remain scarce. Help for Depression (HDep) is one of the few unguided web-based interventions available in Latin America. The results of a use/usability analysis of the original version served as the basis for generating a more user-friendly second version. OBJECTIVE: The aim of this study is to explore participants' satisfaction and acceptability for the second version of HDep. METHODS: A retrospective cross-sectional design was used. An email invitation to complete a web-based survey was sent to all people who accessed HDep in 2018. The questionnaire included satisfaction and acceptability scales and open-ended questions. Complete questionnaires were retrieved from 191 participants: 35.1% (67/191) from those who visited only the home page (home page users [HPUs]) and 6.47% (124/1916) from those who registered to use the program (program users [PUs]). RESULTS: In all groups, users experienced high levels of depressive symptoms (189/191, 98.9%; Center for Epidemiological Studies Scale-Depression >16). Moderate levels of satisfaction (HPUs: mean 21.9, SD 6.7; PUs: mean 21.1, SD 5.8; range: 8-32) and acceptability (HPUs: mean 13.8, SD 3.9; PUs: mean 13.9, SD 3.2; range: 5-20) were found in both groups. Logistic regression analyses showed that among HPUs, women were more satisfied with HDep (odds ratio [OR] 3.4, 95% CI 1.1-10.0), whereas among PUs, older respondents (OR 1.04, 95% CI 1.01-1.08), those with paid work (OR 3.1, 95% CI 2.4-7.6), those who had not been in therapy (OR 2.42, 95% CI 1.09-5.98), and those who had not attempted suicide (OR 3.4, 95% CI 1.1-11.1) showed higher satisfaction. None of the sociodemographic/mental health variables distinguished the acceptability ratings among HPUs. Among PUs, those with paid work (OR 2.5, 95% CI 1.1-5.5), those who had not been in therapy (OR 3.1, 95% CI 1.3-7.3), those without disability (OR 2.9, 95% CI 1.3-6.6), and those who had not attempted suicide (OR 2.6, 95% CI 1.0-6.6) showed higher acceptability. CONCLUSIONS: HDep has good levels of satisfaction and acceptability for approximately half of its users, and the information provided by respondents suggested feasible ways to remedy some of the deficiencies. This qualitative-quantitative study from a low- to middle-income, non-English-speaking country adds to existing knowledge regarding acceptance and satisfaction with web-based interventions for depression in resource-limited countries. This information is important for the creation and adaptation of web-based interventions in low- and middle-income countries, where access to treatment is a major concern, and web-based prevention and treatment programs can help deliver evidence-based alternatives. It is necessary to document the pitfalls, strengths, and challenges of such interventions in this context. Understanding how users perceive an intervention might suggest modifications to increase adherence.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.474
Teacher spread0.414 · 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 designObservational
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

Citations17
Published2021
Admission routes1
Has abstractyes

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