Improving Pain Management Education in US Medical, Nursing, and Pharmacy Schools: Making an Impact on Patient Safety
Bibliographic record
Abstract
Objective: Over 100 million patients in the US deal with some sort of pain. The NIH has considered pain management one of the most urgent problems facing health care today. The number of opioid prescriptions has been increasing over the past several decades and along with this the rate of opioid overdose deaths has nearly tripled in the past fifteen years. Proper pain management by health professionals can lead to better patient outcomes including patient safety. However, evidence has suggested that pain management education is not being sufficiently addressed in pre-licensure programs such as nursing, medicine, and pharmacy. The objective of this study is to examine current practices in pain management education within medical, nursing, and pharmacy schools, identify educational gaps, and make recommendations for improvement in education.\nMethods: This descriptive cross sectional study focused on pre-licensure programs in medicine, nursing, and pharmacy. A two-part survey was administered to schools nationwide to evaluate the depth and breadth of pain education. The first part of the survey focused on questions regarding the scope, quantity, and delivery of pain education. This part was adapted from a previous survey by Mezei and colleagues to assess pain curricula in US and Canadian schools. The second part of the survey focused on opioid education. The survey also assessed different teaching methods that are used, and additional demographic information regarding the participating institutions.\nResults: This study is ongoing with results from the survey still being received. Preliminary results show that the most common teaching methods used are didactic lectures, as well as case-based activities and clinical experience. Few institutions are utilizing team-based and interprofessional learning. When looking at opioid education, results show that programs are consistently providing education about behavorial and non-pharmacological management. However, interventional methods, and opioid contracts are not widely taught. Once the survey is completed, analysis of variance (ANOVA) will be conducted to look for differences between educational program types as well as geographical regions.\nConclusions: Based on early results, it is clear that pain education needs to focus more on patient education, and interprofessional learning opportunities. Improving pain education should not just focus on the quantity of time spent in teaching students, but also the quality of educational experiences delivered. Didactic teaching still seems to be the most common method used, but students would benefit from the opportunity of more interprofessional activities as well as team-based learning. The consensus is that medical, pharmacy, and nursing schools should establish formal pain management education in each year of their curricula. Through improving pain management education, health care professionals of the future will be better prepared to manage their patients, which will directly result in improved outcomes, and patient safety.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".