The Effects of Healthcare on the Opiod Epidemic
Bibliographic record
Abstract
The opioid epidemic has caused nearly 600,000 deaths in the United States and Canada over the past twenty years, with postoperative opioid prescriptions contributing significantly to this crisis. This paper explores the relationship between opioid misuse and postoperative prescribing practices in med-surg units, excluding patients with chronic pain, hospice care, and oncology. It examines whether limiting opioid prescriptions or increasing education for both healthcare providers and patients would be more effective in decreasing the frequency of opioid use disorder (OUD). Key risk factors of OUD include age, gender, socioeconomic status, and comorbidities, along with hospitals and their emergency departments' role in inadvertently aiding opioid misuse. Nonpharmacologic pain management approaches are discussed, including ERAS protocols, NSAID combinations, and complementary therapies, which include relaxation techniques, yoga, music therapy, etc. The nurse's role is accentuated with medication safety, patient advocacy, and providing ongoing education. In the end, reducing opioid misuse in postoperative settings will require a complex approach including education and training, careful prescribing of opioids, and institutional accountability/liability.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".