From Clinical Practice to Academic Student Instruction: Understanding the Clinical Instructor’s Perspective Using a Mixed-Methods Approach
Bibliographic record
Abstract
Background Clinical instructors (CIs) are important to the provision of real-world experiential learning because they teach, mentor, and support students in clinical practice settings in higher education programs. CIs experience tensions that influence their retention and impact the sustainability of consistent, quality education for students. Purpose The aim of this study was to examine the experiences of being a CI and how to better support them. Methods CIs in a nursing faculty at a Western Canadian university were approached to participate. Data collection included a survey ( n = 17) with questions asking about the importance of and their ability to prepare, teach, and mentor nursing students in practice. Individual interviews ( n = 6) and a focus group ( n = 3) were conducted that asked CIs about their experiences and challenges. Analysis included descriptive statistics and thematic analysis. Results Participants indicated feeling unprepared entering the instructor role. Key findings were the need to improve CI orientation so that it is more practical and meaningful, to increase peer support from other instructors, and to assist CIs’ transition into becoming educators. Conclusions Understanding CIs’ assessment of their needs can help institutions better support and retain them, promoting consistency and quality in practicum instruction.
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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 | 0.045 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".