The traditional-faculty supervised teaching model: Nursing faculty and clinical instructors’ perspectives
Bibliographic record
Abstract
Background: The clinical instructors (CI) is an integral part of a quality clinical learning experience. CIs assist nursing students to integrate theory into practice. The traditional faculty-supervised model (traditional model) is used in Canadian undergraduate nursing programs for clinical teaching of Year 1 to 3 students, i.e., one CI supervises 6 to 8 (or 10) nursing students. Some researchers have questioned the effectiveness of the model in preparing students for practice and have concluded that in its current form, it might not be “best practice” with respect to student learning and patient safety. Research is needed to evaluate the traditional model of clinical instruction. Methods: This study explored perceptions and experiences of full-time faculty and CIs who teach and supervise students using the traditional model; and to identify the strengths and challenges of the model with regard to student learning and patient safety. The sample comprised of five faculty and seven CIs. Using an exploratory descriptive approach, qualitative data were gathered through semi-structured interviews and analyzed using thematic content analysis.Results: Although both faculty and CIs described some positive experiences facilitating nursing students’ learning within the traditional model, participants indicated that their experiences depended on the size and complement of the clinical group. Overall, participants perceived more challenges than strengths with the model. Strengths included: (a) peer learning and support, (b) instructors’ familiarity with curriculum and evaluation process, (c) guidance and support for novice students, (d) instructors’ control over students’ learning, and (e) opportunity for clinical experiences in a variety of settings. Challenges included (a) managing large clinical groups, (b) missed learning opportunities, (c) limited time for teaching and supervision, (d) difficulty balancing student learning with patient safety, (e) being seen as visitors on the unit, and (f) lack of role preparation.Conclusions: These findings provide additional evidence to existing knowledge related to clinical education of nursing students. Recommendations for improving the quality of clinical experiences and support for CIs are presented as a means for mitigating some of the challenges of using the traditional model of 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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".