Effectiveness of Web-based Cognitive Bias Intervention for Psychiatric Disorders: A Systematic Review Protocol (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Background: Traditional psychological therapies focus mainly on modification of individuals’ conscious decision-making process. Unconscious processes such as cognitive biases have been found to be accountable for various psychiatric psychopathologies. The advances in technologies have transformed how bias modification programs are being delivered. Objective: We seek to synthesize the current evidence of web-based cognitive bias modification for psychiatric disorders, by identifying the range of conditions targeted and their current efficacy. We wish to determine if web-based attention bias modification is as efficacious as compared to conventional methods. Methods and analysis: A systematic review will be conducted, and all studies types will be included. There will not be any restrictions on the participants included in the study. A search will be conducted on the respective databases up till 2017. Selection of studies will be by the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA-P) guidelines. Quality assessment of the included studies will be assessed using the Cochrane Risk of Bias tool (for randomized trials) and the Newcastle-Ottawa scale for other study designs. A narrative synthesises of the identified articles will be conducted. A meta-analysis will be considered, only if there are sufficient articles in a domain for statistical analysis. Ethical approval for the current protocol and the planned systematic review was not required. Results: Results synthesized would be disseminated using conference presentation or published works in peer-reviewed journals. Conclusions: This review is of importance given how technology transformed the delivery of conventional therapies. The findings from this review will provide guidance for future research involving technology and cognitive bias modification interventions. Registration details: International Prospective Register for Systematic Reviews (PROSPERO) number 2017 CRD42017074754
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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 | 0.050 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.087 | 0.012 |
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".