Inducing bias modulates sensitivity to nonverbal cues of others' pain
Bibliographic record
Abstract
BACKGROUND: There is ample research to support the existence of bias in the perception of others' pain. Both studies involving health-care professionals and student surrogate samples have found that, firstly, pain is under-perceived when using nonverbal cues to gauge another's suffering and, secondly, that personal characteristics of both the viewer and the target (such as gender) can bias pain perception, affecting the allocation of help. However, the extant research shows conflicts about the direction of the bias that target gender exerts on pain perception. Our study aims to address these challenges by examining whether under-perception of pain can be attenuated or exacerbated with gender primes and how target gender affects nonverbal pain perception, in particular. METHODS: University students (N = 120) were primed with either masculine, neutral, or feminine concepts followed by photos of male and female targets displaying various levels of pain and asked to quantify the photographed targets' distress. RESULTS: Participants perceived lower target distress when this task was preceded by a masculine gender prime, as compared to a neutral or feminine gender prime. Pain was underestimated for all targets; however, this underestimation was significantly more pronounced for female targets. CONCLUSIONS: These results suggest that gender cues may influence the perception of observed pain and, as a result, clinical decision making. They also support the conjecture that nonverbal pain cues may be under-perceived in women.
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How this classification was reachedexpand
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 | 0.092 | 0.023 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".