Nursing faculty perceptions of student faculty interactions
Bibliographic record
Abstract
Objective: Student-faculty interaction outside the classroom in higher education is a well-studied phenomenon and is linked directly to office hours. Research has shown the significance of these interactions on student success; however, underuse of office hours remains a problem. Historical research has examined perceptions of students while fewer address faculty. There is limited investigation into nursing, where students must be successful on high stakes NCLEX testing after graduation. This study investigated nursing faculty perceptions of student-faculty interaction outside the classroom in relation to office hours.Methods: A qualitative design elicited responses from full time nursing faculty at one university school of nursing in the southeastern United States. Ten participants were interviewed using a semi-structured script. Data analysis revealed nursing faculty perceptions in relation to office hours.Results: The following themes emerged in relation to office hours and nursing faculty perceptions: (a) “At any point my door is always open”, (b) “I like having that flexibility, it does help”, and (c) “I’m basically 24/7. I really am”. Technology was embedded throughout the themes. Some limitations existed, such as reflexivity of the researchers, small sample size, and final sample bias.Conclusions: Findings from the study can guide policies in higher education, specifically the way office hour mandates are implemented. Increasing student-faculty interaction outside the classroom is a worthwhile goal that is important in schools of nursing where success on high stakes NCLEX testing reflects the integrity of the school.
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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 | 0.005 | 0.020 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".