Developing and Piloting an Online Graduate Nursing Course Focused on Experiential Learning of Qualitative Research Methods
Bibliographic record
Abstract
Despite the turmoil of a worldwide economic crisis, the health sector remains largely understaffed, and the nursing shortage represents a major issue that jeopardizes graduate nursing education. Access to education remains a challenge, particularly in rural and remote areas. This article reports the process of developing an asynchronous online qualitative research course. This online course was piloted among 16 interdisciplinary students. Participants agreed that experiential learning was useful to understand the intricacies of qualitative research. Within this constructivist approach, students were immersed in real-life experiences, which focused on the development of skills applicable to qualitative research. Based on the findings, we suggest that constructivism and the Four-Component Instructional Design (4C/ID) model (a four-part approach for fostering the development of complex skills) represent valuable ontological and pedagogical approaches that can be used in online courses. Triangulating these two approaches is also congruent with the student-centered philosophy that underpins nursing graduate programs.
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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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".