Phenomenological Characteristics of Attention Bias Modification Apps: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
Attentional bias has been purported to be responsible for several psychiatric disorders such as anxiety, post-traumatic stress, and substance abuse. To address the problems experienced by patients, attention bias modification training (ABMT) is commonly used as a form of treatment. Yet, the accessibility of this treatment still remains a challenge. Recent studies have proposed app-based ABMT leveraging the popularity and convenient use of smartphones. While past reviews have explored the design methods and their efficacy, there remains a lack of systematic evaluation of the phenomenological characteristics of the ABMTs offered. This study used systematic review and meta-analytic procedures to investigate the effect of ABMT on attention biases and mental health symptoms. The novelty of the study is the investigation of the phenomenological characteristic of app-based ABMT that contributes to its efficacy.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.027 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".