An innovative and qualitative research methodology course in graduate master nursing program of Southern Taiwan
Bibliographic record
Abstract
Background: A quantitative worldview has long dominated nursing research, causing it to lack the humanized inspiration of a qualitative worldview. This study sought to develop and evaluate an innovative and qualitative research methodology course in one graduate master nursing program of Southern Taiwan.Methods: Qualitative research design was to develop the innovative and qualitative research methodology course with training the Listening, Empathy, and Presence (LEP) skills and the qualitative worldview of master nursing students. This study was approved by the improved teaching project grant from a University (FYU1008-109-04) during March 1 to October 31, 2020.Results: The results identified that first, the innovative and qualitative research methodology course incorporated the course objectives, teaching content, and innovative strategies in two elective credits with content validity as .86. Additionally, the nine master nursing students reported that the quantitative worldview was closely associated with numbers, statistics, and measurement, whereas the qualitative worldview had a greater tendency to entail aspects of humanism with conversations, communication, and caring among nine master nursing students.Conclusions: The innovative and qualitative research methodology course could train master nursing students to master qualitative research skills and help nursing educators achieve balance with mutual coexistence and understanding between qualitative and quantitative approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".