Bibliographic record
Abstract
Preceptorship is widely used as a cost-effective clinical non-traditional teaching method. However, insufficient research has been done in this area, particularly as to how a successful student-preceptor relationship is formed. The rural setting poses additional challenges as the nursing instructor is not physically present to monitor the course of the student-preceptor relationship or to resolve arising boundary issues. This is part one of a grounded theory project whereby eleven rural preceptors were asked ‗what kinds of professional boundaries do you create in the rural preceptorship experience‘ and ‗how they created and maintained professional boundaries while precepting nursing students‘. The research project consisted of two parts: each examining the perspectives of preceptors and students. However, this study will focus on the perceptions of preceptors and is the first to examine perceptions of preceptors in the area of teaching and boundaries in rural settings. The resulting core variable was: trusting the student to be safe and the psychosocial process was the relationship they developed with the student.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".