A Self-Administered Multicomponent Web-Based Mental Health Intervention for the Mexican Population During the COVID-19 Pandemic: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has become a public health emergency of international concern; it has not only threatened people's physical health but has also affected their mental health and psychological well-being. It is necessary to develop and offer strategies to reduce the psychological impact of the outbreak and promote adaptive coping. OBJECTIVE: This study protocol aims to describe a self-administered web-based intervention (Mental Health COVID-19) based on the principles of positive psychology supported by elements of cognitive behavioral therapy and behavioral activation therapy to reduce the symptoms of anxiety and depression and increase positive emotions and sleep quality during and after the COVID-19 outbreak through a telepsychology system. METHODS: A randomized controlled clinical superiority trial with two independent groups will be performed, with intrasubject measures at four evaluation periods: pretest, posttest, 3-month follow-up, and 6-month follow-up. Participants will be randomly assigned to one of two groups: self-administered intervention with assistance via chat or self-administered intervention without assistance via chat. The total required sample size will be 166 participants (83 per group). RESULTS: The clinical trial is ongoing. This protocol was approved by the Research Ethics Board of the Free School of Psychology-University of Behavioral Sciences (Escuela libre de Psicología-Universidad de Ciencias del Comportamiento). The aim is to publish the preliminary results in December 2020. A conservative approach will be adopted, and the size effect will be estimated using the Cohen d index with a significance level (α) of .05 (95% reliability) and a conventional 80% power statistic. CONCLUSIONS: The central mechanism of action will be to investigate the effectiveness of an intervention based on positive psychology through a web platform that can be delivered through computers and tablets, with content that has been rigorously contextualized to the Mexican culture to provide functional strategies to help the target users cope with the COVID-19 pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT04468893; https://clinicaltrials.gov/ct2/show/NCT04468893. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/23117.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".