Enhancing Knowledge and Attitudes Regarding Opioid Use Disorder Among Private Primary Care Clinics
Bibliographic record
Abstract
ABSTRACT: Opioid use disorder (OUD) continues to impact communities worldwide. British Columbia specifically declared a public health emergency in April 2016. It is known that patients with OUD often experience barriers in access to care, including limited knowledge and training among providers, as well as persisting stigma in the medical community. The Doctor of Nursing Practice quality improvement project sought to provide barrier-targeted OUD education while using multiple effective teaching methods, such as test-enhanced learning, to family nurse practitioners (FNPs) working among private primary care clinics to assess the impact on knowledge and attitudes. In review of an experience survey, zero participants had received prior education on OUD (N = 7). The Drug and Drug Problems Perceptions Questionnaire was used to assess attitudes. In review of the data, attitudes before receiving education (Mdn = 74) improved after receiving barrier-targeted education (Mdn = 66), W = 0, p < .05. Knowledge was tested at three time points. After a review of unique identifiers, four participant tests were successfully linked. It was found that knowledge after receiving education (M = 7.75, Mdn = 7.5) improved in comparison with baseline knowledge (M = 6, Mdn = 6) and further improved after a 1-month time frame (M = 8.5, Mdn = 8.5). Although the project was limited by sample size, providing education to FNPs who have not received prior education on OUD, and using modalities such as test-enhanced learning, showed a favorable impact on knowledge and attitudes. In light of the opioid epidemic, nursing leaders must continue to actively engage practicing FNPs and students with OUD education. FNPs are well positioned to be champions in this area and may mobilize teams to overcome barriers among private primary care clinics and increase access to care.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".