The relational narrative: implications for nurse practice and education
Bibliographic record
Abstract
Nurses frequently encounter situations in which they are compelled to make ethical decisions about what is good and right to do in their day-to-day practice. Often existing moral edicts prove to be inadequate in light of the patient's particular circumstances. To what, then, can the nurse turn? In response to this question, Gadow (1999) proposes a dialectical framework comprised of three ethical approaches: subjective immersion (ethical immediacy), objective detachment (ethical universalism), and intersubjective engagement (relational narrative). In this paper, the dialectic framework proposed by Gadow (1999) is examined with respect to some of its potential implications for nursing practice and education. This endeavour is undertaken in the spirit of extending the dialogue that Gadow has so eloquently initiated. Questions related to whether it is possible (or necessary) to reconcile the contradictory tenets of these ethical approaches or the potential disjunction between the knowledge embedded in the relational narrative and ensuing action on the part of the nurse are discussed. In addition, some of the potential implications of this dialectical framework for nurse education are explored, including the question of how nurses learn to enter into a relational narrative with another and the relevance of relational narratives to teacher-student relationships.
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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 | 0.030 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.006 |
| 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".