Contextual influences in decoding pain expressions: effects of patient age, informational priming, and observer characteristics
Bibliographic record
Abstract
We aimed to examine the effects of contextual factors (ie, observers' training background and priming texts) on decoding facial pain expressions of younger and older adults. A total of 165 participants (82 nursing students and 83 nonhealth professionals) were randomly assigned to one of 3 priming conditions: (1) information about the possibility of secondary gain (misuse); (2) information about the frequency and undertreatment of pain in the older adult (undertreatment); or (3) neutral information (control). Subsequently, participants viewed 8 videos of older adults and 8 videos of younger adults undergoing a discomforting physical therapy examination. Participants rated their perception of each patient's pain intensity, unpleasantness, and condition severity. They also rated their willingness to help, sympathy level, patient deservingness of financial compensation, and how negatively/positively they feel towards the patient (ie, valence). Results demonstrated that observers ascribed greater levels of pain and other indicators (eg, sympathy and help) to older compared with younger patients. An interaction between observer type and patient age demonstrated that nursing students endorsed higher ratings of younger adults' pain compared with other students. In addition, observers in the undertreatment priming condition reported more positive valence towards older patients. By contrast, priming observers with the misuse text attenuated their valence ratings towards younger patients. Finally, the undertreatment prime influenced observers' pain estimates indirectly through observers' valence towards patients. In summary, results add specificity to the theoretical formulations of pain by demonstrating the influence of patient and observer characteristics, as well as informational primes, on decoding pain expressions.
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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 | 0.006 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".