Case Managers on the Front Lines of Opioid Epidemic Response
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to examine how case managers, taking a holistic, patient-centered approach that is grounded in advocacy, have a crucial role to play in the opioid crisis response. This includes providing education, support, and resources to prevent misuse of and addiction to opioids prescribed for pain management and intervening with more resources to help combat the nonmedical use of prescription opioids and heroin. PRIMARY PRACTICE SETTINGS: In addition to case managers in acute care, workers' compensation, and palliative care, who have frequent contact with patients who are prescribed opioid medications for pain management, all case managers may interact with patients and support systems/families who are directly or indirectly impacted by opioid use, misuse, and addiction. IMPLEMENTATIONS FOR CASE MANAGEMENT PRACTICE: The broad scope of the opioid epidemic necessitates individualized interventions to address the multiple needs of individuals. The case manager, particularly one who is board-certified, has the expertise and knowledge to assess individual needs, identify treatment and other resources, and provide education and support to the patient and family/support system. In addition, given the complexity and life-or-death consequences associated with the opioid crisis, a timely and comprehensive approach is essential, bringing together multiple disciplines in health care, public health, addiction, pain management, social work, mental health counseling, pharmacology, and case management.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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".