Precepting in critical care: Communication and collaboration
Bibliographic record
Abstract
Well-honed interpersonal and communication skills are essential in an effective preceptorship. Preceptors not only facilitate and develop positive interpersonal relationships between themselves, the preceptee, the patient and family, and the healthcare team, but they have a responsibility to foster and develop the preceptee’s interpersonal and communication skills as well. Doing so is even more important and more challenging in the complex, busy, and often overwhelming critical care environment. This article is the third in a series that aims to support new and experienced critical care nurse preceptors, as they guide those who will become the future of critical care nursing. Effective interpersonal and communication skills are essential to establish any constructive professional relationship in nursing practice, but even more so in the critical care environment due to its fast-paced, highly acute and complex nature. Effective communication skills help build trusting relationships, foster a caring environment, allow greater accuracy and understanding of information and, ultimately, lead to safer patient- and family-centred care (Arnold & Boggs, 2020). Strong interpersonal and communication skills can assist preceptors to be more effective personally and even more so in their role guiding preceptees (Hardie et al., 2022). At the same time, preceptees may have limited experience with communication in clinical practice, particularly in the high-pressure context of an intensive care unit (ICU), and it is the preceptor’s responsibility to assist their preceptees in developing these skills.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".