Implementing motivational interviewing training: Strengthening the role of the registered nurse
Bibliographic record
Abstract
The current primary healthcare model is in need of urgent transformation, as it is vital to addressing health priorities at the systemic level. With over 3 million members, the nursing profession is the largest entity of the nation’s healthcare workforce, however they are underutilized in the primary care setting. As the pendulum swings back towards community based primary care, changes in nursing education are critical for support. Nursing curricula needs to be reexamined, updated, and adaptive enough to change with patients’ needs and improvements in both science and technology. The Josiah Macy Jr. Foundation has identified and responded to these needs with specific themes and recommendations on how best to prepare the registered nurse in the enhanced team member role. The purpose of this article, is to further explore the education of nursing students with a focus in primary care and consider the use of motivational interviewing, an evidenced-based intervention, to bridge a gap in nursing curricula. Faculty members are in a strategic position to educate and introduce the nursing future about the use of motivational interviewing, to help patients make healthier choices and impact future health outcomes.
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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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".