Nurses’ perceptions related to impaired nurses and knowledge of substance use disorder within the nursing profession
Bibliographic record
Abstract
Objective: Substance Use Disorder continues to be a problem within the nursing profession. Studies have been conducted to examine various aspects of Substance Use Disorder. Although programs have been implemented to help rather than punish nurses, knowledge barriers that affect reporting still exist. The purpose of this study was to examine nurses’ perceptions of impaired nurses, perceptions of reporting an impaired nurse, and perceptions of knowledge regarding Substance Use Disorder within the nursing profession.Methods: A mixed-method descriptive study was conducted using the Perception of Nursing Impairment Inventory tool, as well as three open-ended questions. Lazarus and Folkman’s Transactional Model of stress and coping was used to guide the study and nurses from one state’s nurses’ association in the southeastern region of the United States participated.Results: Based on quantitative findings, most disagreed with the statement there is little that can be done to help impaired nurses and agreed that nurses have an ethical obligation to report if impairment is suspected. Conflicting views were identified for multiple statements on the Perception of Nursing Impairment Inventory. The qualitative findings revealed average or below average knowledge of SUD and identified barriers that may affect reporting. Further, a culture surrounding the ethical dilemma of reporting was evident.Conclusions: Educational gaps exist between recognizing and reporting the problem of Substance Use Disorder. Although nurses acknowledge an obligation to report, many barriers to reporting were identified. Recommendations were made for additional qualitative research related to nursing education including conflicted feelings about doing what is “right.”
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".