Keeping the Center of Nursing Alive: A Framework for Preceptor Discernment and Accountability
Bibliographic record
Abstract
This research served to explicate the profile of the nurse preceptor and illuminate the knowledge that nurse preceptors accrue while teaching students in the final clinical practicum. The analysis of the descriptive survey, sent to approximately 770 preceptors, along with the interpretive analysis of 16 focus groups and 5 interviews, revealed understandings of discovering, learning, and engaging as central to the relational elements of professional discernment and accountability. Preceptor discernment is essential to ensuring competent nursing practice for student nurses preparing for entry-level practice. Dissemination of the findings is suggested through the creation of an educational framework reflective of, relevant to, and used by nurse preceptors that may be used in both practice and academic settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".