Clinical Instructors' Experiences in Facilitating and Evaluating Nursing Students in a Professional Practice Course
Bibliographic record
Abstract
Aim: This study aimed to gain a deeper understanding of the experiences of a clinical instructor in facilitating and evaluating the performance of students in a professional practice course at one of Ontario's Universities. Background: In professional practice courses for nursing students, clinical instructors serve as vital guides, helping students apply theoretical knowledge to real-world clinical settings. The experiences of clinical instructors in these courses can significantly impact nursing students' learning outcomes. The clinical instructors are generally employed part-time by a university and are assigned to teach professional practice courses, typically for a semester. Many clinical instructors are often hired without prior teaching experience, and the lack of clinical teaching orientation deems them unprepared for the job. These challenges may hinder their effectiveness as clinical instructors, impacting the quality of clinical teaching and student learning experiences. Therefore, it is crucial to study this group of clinical instructors to identify their unique needs, challenges, and experiences. Design: This qualitative study, conducted in 2022 and 2023, utilized an interpretive description research approach for data collection and analysis. Purposive sampling was employed, and all eligible clinical instructors in the nursing program were invited to participate. Twelve participants provided consent and were interviewed using a semi-structured questionnaire. Results: A content analysis of the data provided an insight into their experiences. The participants voiced concerns about the inconsistency and uncertainty surrounding course expectations, expressing a desire for more explicit guidelines. Many instructors admitted the challenge of addressing student failure in clinical settings due to the complexity of evaluation tools, which often include numerous parameters. Many participants expressed frustration over the lack of performance evaluation and feedback, which left them needing clarification about the effectiveness of their role. The additional strain of coping with increased workload during the pandemic further compounded their challenges, underscoring the need for adequate support and resources. Conclusions: Overall, the experiences shared by the participants emphasize the multifaceted nature of their role and the importance of providing the necessary tools, support, and resources to effectively facilitate and assess students' clinical experiences. This highlights the need for policy changes and resource allocation to enhance the quality of clinical teaching.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".