Relational Inquiry—Attending to the Spirit of Nursing Students
Bibliographic record
Abstract
The impetus for this paper came from our experiences as learner-teachers of re-considering the epistemological and ontological roots of our undergraduate-nursing curriculum. It began as an earnest dialogue regarding particular aspects of first year undergraduate-nursing theory content, specifically, caring and compassion, self-concept and nursing identity, spirituality and culture, a simple question—how could we better engage first year nursing students with what they frequently considered to be “abstract” and “soft” concepts? An organic need to be “good teachers” and introduce learners to fundamental concepts in nursing, and have them understand, in meaningful ways, the complexity of “caring and compassion” with respect to what it is that nurses do, think, and enact. To this end, we enlisted Relational Inquiry, as articulated by Gweneth Hartrick Doane and Colleen Varcoe, as a means of creating an epistemological and ontological foundation for our teaching practice in order to better support the development of critically reflective, community orientated, caring relational practitioners. Initially, we thought relational inquiry was an epistemological endeavor and found that it is an ontological undertaking. We discovered that practicing from a relational caring perspective shifted our focus from the content to the student as a developing practitioner and human being. Through the process of re-imagining our teaching practice, we have begun to re-consider the importance of “attending to the spirit” of nursing students.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".