Bibliographic record
Abstract
BACKGROUND: The trend toward humanistic nursing education has called for a transformed student-teacher relationship that fosters learning and growth of students and teachers. Although such a relationship has been claimed to form the basis for student-teacher connection and to be a positive influence on students' learning outcomes, there is a paucity of research exploring these claims. Neither the nature of student-teacher connection nor the processes by which it occurs have been described. AIMS: A research study was undertaken to explore and describe undergraduate nursing students' experiences of connection within the student-teacher relationship and the effects of student-teacher connection on students' learning experiences in clinical nursing education. RESEARCH DESIGN: The qualitative research approach of interpretive description was chosen for this study. Unstructured interviews and a focus group were used to collect data from eight undergraduate nursing students. Data were analysed using the process of constant comparative analysis, and revealed four interrelated major categories that formed a description of the students' experience of student-teacher connection. FINDINGS: This article presents part of the findings of this study. After describing the nature of student-teacher connection, the discussion focuses on the influence of teachers and other factors on the formation of student-teacher connection. Relevance is given to this discussion by describing the outcomes of connection for students' clinical learning experiences.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".