When We Should Worry More: Using Cognitive Bias Modification to Drive Adaptive Health Behaviour
Bibliographic record
Abstract
A lack of behavioural engagement in health promotion or disease prevention is a problem across many health domains. In these cases where people face a genuine danger, a reduced focus on threat and low levels of anxiety or worry are maladaptive in terms of promoting protection or prevention behaviour. Therefore, it is possible that increasing the processing of threat will increase worry and thereby enhance engagement in adaptive behaviour. Laboratory studies have shown that cognitive bias modification (CBM) can increase or decrease anxiety and worry when increased versus decreased processing of threat is encouraged. In the current study, CBM for interpretation (CBM-I) is used to target engagement in sun protection behaviour. The goal was to investigate whether inducing a negative rather than a positive interpretation bias for physical threat information can enhance worry elicited when viewing a health campaign video (warning against melanoma skin cancer), and consequently lead to more adaptive behaviour (sun protection). Participants were successfully trained to either adopt a positive or negative interpretation bias using physical threat scenarios. However, contrary to expectations results showed that participants in the positive training condition reported higher levels of worry elicited by the melanoma video than participants in the negative training condition. Video elicited worry was, however, positively correlated with a measure of engagement in sun protection behaviour, suggesting that higher levels of worry do promote adaptive behaviour. These findings imply that more research is needed to determine under which conditions increased versus decreased processing of threat can drive adaptive worry. Various potential explanations for the current findings and suggestions for future research are discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".