Assessing the efficacy of a manual‐based intervention for improving the detection of facial pain expression
Bibliographic record
Abstract
BACKGROUND: This article presents the results of a parallel-group, non-randomized, controlled study that evaluated the feasibility of an online training program for improving observer detection of facial pain expression. METHOD: Fifty-four undergraduate students attended two laboratory sessions interspersed by an intervention period where they were assigned to complete the Index of Facial Pain Expression (IFPE)-an online training environment designed to teach observers to code facial muscle movements associated with pain-or a no-contact control. Participants completed questionnaires during the first session and watched parallel versions of the Sensitivity to Expression of Pain (STEP) test during laboratory sessions. STEP tests contained excerpts of facial expressions taken from patients with shoulder pain. Reliability of coding following the IFPE was measured. Signal detection methods were applied to pain ratings to the STEP tests to calculate measures of sensitivity and response bias to facial pain expression. RESULTS: Participants took 3.5 hr to complete the IFPE. Training resulted in reliable coding of facial muscle movements associated with pain and improvements in sensitivity (from 0.75 to 0.87 in experimental relative to 0.75 to 0.80 in control), but not response bias, to facial expressions of clinical pain. Training was influenced by observer traits, including empathy, emotional intelligence (EI), and prior experience with individuals who experience chronic pain. CONCLUSIONS: The IFPE represents a brief measurement system for facial pain expression with research applicability and potential clinical utility. The IFPE could help clinicians be more sensitive to expressions of clinical pain. SIGNIFICANCE: The index of facial pain expression (IFPE) is an online training program that can improve an observer's ability to reliably detect expressions of clinical pain after as few as 3.5-hr of training.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".