“Turning the tide” in Treatment of substance use: A nursing response
Bibliographic record
Abstract
Objective: To analyze science and practice surrounding nursing approaches to substance use disorders (SUDs) and make recommendations for the future.Methods: A review of literature and topics related to healthcare provider stigma, science surrounding SUDs, nursing approaches to SUDs in education and practice and evidence based treatment was conducted, analyzed and synthesized.Results: Stigma is embedded in nursing approach to SUDs, up to date information regarding SUDs is not widely disseminated or practiced in nursing.Conclusions: To reduce the impact of stigma and to bring nurses into the “turning the Tide” movement requires an understanding of how beliefs root stigma, building knowledge related to SUDs as an illness, and expansion of nurses' skill when intervening with individuals dealing with SUDs.
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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 | 0.034 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".