Attentional Bias to Threat-Related Information Among Individuals With Dental Complaints: The Role of Pain Expectancy
Bibliographic record
Abstract
Expecting pain can be perceived as a threat may involve recruitment of cognitive strategies (such as attentional avoidance) which might help the person to reduce distress. The ecological validity of the paradigms aiming to study the attentional biases towards or away from threatening stimuli by manipulating the perception of threat in experimental settings has been questioned. Therefore, the current study aims to investigate the attentional bias toward or away from the threat when a confrontation with a real threatening and painful condition would be expected (i.e., dental treatment). One hundred and twenty-seven patients referred to three dentistry clinics for a dental treatment (experiment participants) and 30 individuals with no dental complaints (control participants) completed this study. Patients were randomly allocated to a high (HPE: n=65) or a low pain (LPE: n=62) expectancy condition. All participants completed questionnaires of distress, fear of pain, and fear of dental pain. Furthermore, they participated in a dot-probe task that assessed their attention to painful faces, dental pictures, and happy faces. In addition, before the treatment, participants reported their anticipated pain intensity and after the treatment, they reported the pain intensity that they perceived during the treatment using two separate visual analogue scales. Patients in the HPE group showed a bias away from dental pictures compared to LPE and control group participants. HPE group patients also reported greater pain intensity during the treatment compared to LPE patients. Greater attentional bias away from dental pictures among HPE patients was associated with higher levels of fear of pain, fear of dental pain, and stress. Avoidance of highly salient threatening images can be seen as an unhelpful emotion-regulation strategy that individuals use to manage their fears. However, in this study, avoidance was associated with poorer outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".