The Postoperative Pain Assessment Skills Pilot Trial
Bibliographic record
Abstract
UNLABELLED: BACKGROUND⁄ OBJECTIVES: Pain-related misbeliefs among health care professionals (HCPs) are common and contribute to ineffective postoperative pain assessment. While standardized patients (SPs) have been effectively used to improve HCPs' assessment skills, not all centres have SP programs. The present equivalence randomized controlled pilot trial examined the efficacy of an alternative simulation method - deteriorating patient-based simulation (DPS) - versus SPs for improving HCPs' pain knowledge and assessment skills. METHODS: Seventy-two HCPs were randomly assigned to a 3 h SP or DPS simulation intervention. Measures were recorded at baseline, immediate postintervention and two months postintervention. The primary outcome was HCPs' pain assessment performance as measured by the postoperative Pain Assessment Skills Tool (PAST). Secondary outcomes included HCPs knowledge of pain-related misbeliefs, and perceived satisfaction and quality of the simulation. These outcomes were measured by the Pain Beliefs Scale (PBS), the Satisfaction with Simulated Learning Scale (SSLS) and the Simulation Design Scale (SDS), respectively. Student's t tests were used to test for overall group differences in postintervention PAST, SSLS and SDS scores. One-way analysis of covariance tested for overall group differences in PBS scores. RESULTS: DPS and SP groups did not differ on post-test PAST, SSLS or SDS scores. Knowledge of pain-related misbeliefs was also similar between groups. CONCLUSIONS: These pilot data suggest that DPS is an effective simulation alternative for HCPs' education on postoperative pain assessment, with improvements in performance and knowledge comparable with SP-based simulation. An equivalence trial to examine the effectiveness of deteriorating patient-based simulation versus standardized patients is warranted.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".