Phenomenological Characteristics of Attention Bias Modification Apps: A Systematic Literature Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it