Preceptors Matter: An Evolving Framework
Bibliographic record
Abstract
Preceptors teach students within complex, unpredictable, and often chaotic environments. The teaching expertise that preceptors acquire as they guide, facilitate, and evaluate student learning often is overlooked by both academia and service. The purpose of this triangulated research was to create a profile of nurse preceptors and reveal teaching expertise through the interpretation of preceptors' everyday experiences and challenges. The findings of this research are brought forward through the three main understandings of discovering, learning, and engaging. Dissemination occurred through the development of a collaborative Centralized Preceptorship Education Project that included three health regions, seven academic institutions, and professional regulating bodies, as well as the development of a preceptor educational framework, entitled Preceptors Matter. Our intent throughout the research and dissemination process was to legitimize the preceptor role by revealing expertise, connecting conversations, and offering opportunities for extension.
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 | 0.046 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.073 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".