Effective Nursing Recovery-Oriented Interventions for Individuals With Substance Use Disorder
Bibliographic record
Abstract
ABSTRACT: Nurses support the recovery of individuals with substance use disorder. How they support individuals, however, may impact the effectiveness of their work. For example, there are various paradigms of recovery that alter interventions. In addition, negative attitudes adopted by clinicians discourage individuals who use substances from accessing healthcare services, experiencing further health deterioration. Alternatively, nurses can enact interventions that promote positive experiences, further supporting the recovery of individuals. Hence, it is beneficial to increase nurses' awareness of effective interventions that promote recovery. The purpose of this literature review is to examine effective nursing interventions that promoted recovery of those with substance use disorders from the perspective of nurses and individuals who received nursing care. The review identified that effective interventions were based on three major themes: person-centered care, empowerment, and maintaining supports and capability enhancement. In addition, literature revealed that some interventions were perceived to be more effective; this depended on whose viewpoint was examined-nurses or individuals with substance use disorders. Finally, there are interventions based on spirituality, culture, advocacy, and self-disclosure that are often disregarded but may be effective. Nurses should utilize the more prominent interventions as they offer the most benefit and integrate interventions that are often overlooked.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".