Trench work: Scaffolding a metaphorical bridge to foster the advancement of caring sciences
Bibliographic record
Abstract
Caring, as knowledge, must be foundational within a nursing curriculum; while caring, as action, must be consistently nurtured in caring relationships to foster healing environments. If caring is seen as a way of being that emanates from a nurse’s expression of their humanity then knowledge of caring sciences is vital in nursing education to ensure the development of an ethical, epistemological, and ontological perspective for both educators and students. One approach to ensuring the actualization of a caring science curriculum is through authentic dialogue, thereby, embodying the prerequisites necessary for a nursing student to espouse a caring practice is imperative. A novel approach to curriculum development evolved following a candid discussion between nursing students and faculty with respect to a core course offered in an undergraduate nursing program. This discussion inspired the co-development of case studies grounded in the caring sciences exploring the concepts of moral dilemmas, spirituality, and suffering. As nursing faculty, we have the opportunity and responsibility to create and role-model caring relationships with our students to enhance their future nursing practice and to continue to nurture our own professional development; influencing knowing, doing, and being as a caring practitioner. The authors, an enrolled undergraduate nursing student (during the research study) and a tenured faculty member, will reflect on their journey of scaffolding a metaphorical bridge (initiating, developing, and sustaining an innovative academic collaboration) grounded in caring sciences to enhance undergraduate nursing education.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".