Designing a family health nursing online course: Weaving accessible pedagogy approaches
Bibliographic record
Abstract
We are writing this paper to report on students’ and faculty experiences in an online family nursing health course based on 'relational inquiry', that is, a way of thinking that situates individuals within their cultural and social context. Research shows a gap exists between what nursing students are taught and what they later find out nursing really is. In addition, with the increased use of online nursing core courses in the curriculum, facilitating accessability and engaging the diverse learner in the complex nursing realities can be even more challenging. During the last 5 years, we have been teaching a family health assessment course using Doane and Varcoe’s (2015) relational inquiry concepts for Registered Practical Nurses (RPN) bridging to a Bachelor of science in Nursing (BScN). Teaching relational inquiry concepts fully online has been challenging in terms of helping the diverse learner access and navigate the complex nursing workplace realities and form a virtual relationship with self, peers, faculty, and the online environment. Through the use of families’ who are situated in diverse culture and context, relational inquiry acknowledges nurses’ workplace realities and then offers students ways to navigate this complexity. Thus, the purpose of this paper is to describe four accessible pedagogy approaches we included in our online course (e.g., creating a video or transcript; posting or replying to an article; joining a synchronized discussion; and participating in a non-synchronized discussion) and report students’ and faculty experiences from a relational inquiry lens.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".