Connecting Theory to Practice: Using Guided Questions to Standardize Clinical Postconference
Bibliographic record
Abstract
BACKGROUND: Assisting nursing students with the integration of theoretical knowledge in the practice setting can be a challenge for clinical instructors. Clinical instructors using planned questions to guide discussion among students during postconference is one method that can be used to achieve this goal. Open-ended guided questions that deliberately address and synthesize classroom knowledge during postconference discussions are advantageous to both students and clinical instructors. METHOD: The purpose of this article is to describe the process of standardizing the weekly postconference by deliberately integrating questions within a second-year nursing clinical course at a Canadian university. RESULTS: In this course, the guided questions provided clinical instructors who facilitated the postconferences with an opportunity to enhance their own level of comprehension and currency in various subject areas, as well as evaluate students' critical thinking and knowledge gaps. CONCLUSION: Understanding the nursing curriculum and providing clinical instructors with the appropriate skills to facilitate postconference discussions were paramount to the success of these standardized postconferences.
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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 | 0.094 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".