Whole Person Teaching Makes an Effective Baccalaureate Nursing Teacher: Student Voices Enlighten Us
Bibliographic record
Abstract
Objectives: The goals of this study were to understand: 1) what makes an effective teacher in each level of the baccalaureate nursing program, 2) what are the skills, attributes and strategies of an effective teacher in both theory and clinical courses, and 3) how does this impact student learning and the student experience in each level of the curriculum?Methods: A qualitative description approach was used. All BScN students at two sites (1000 students) were emailed an invitation to participate in an online survey to identify what makes an effective teacher in the baccalaureate nursing program. Students were also invited to participate in focus groups to discuss what makes an effective teacher and the impact on their learning. It is anticipated that six focus groups of 10 to 12 students will be conducted. Survey and focus group data are analyzed using qualitative content analysis.Findings: A preliminary finding emergent from the data is that students perceive whole person teaching, that is understanding the learner as a whole person, makes an effective teacher. This study has the potential for important impact on students and faculty in baccalaureate nursing programs. The results will be used to plan faculty development initiatives throughout all levels within relevant programs. Findings, conclusions and recommendations will be shared at the conference.Conclusions: Will be available at the time of the conference. Faculty members are learning how to optimally facilitate learning in a new context that embraces a person-centered, problem based, self-directed and small group learning approach. Students are in the best position to articulate what makes an effective teacher in each year of the four year program. Students were eager and empowered to share their perceptions and faculty eager to learn from student voices to optimize student experiences and learning.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".