Brave spaces in nursing ethics education: Courage through pedagogy
Bibliographic record
Abstract
BACKGROUND: Nursing students must graduate prepared to bravely enact the art and science of nursing in environments infiltrated with ethical challenges. Given the necessity and moral obligation of nurses to engage in discourse within nursing ethics, nursing students must be provided a moral supportive learning space for these opportunities. Situating conversations and pedagogy within a brave space may offer a framework to engage in civil discourse while fostering moral courage for learners. RESEARCH OBJECTIVE: The aim of this research is to explore the influence of a structured self-assessment tool called the ESA "Engagement Self-Assessment" on the alignment and creation of a brave space in a nursing ethics course. RESEARCH DESIGN: This study used an exploratory, cross-sectional survey design. PARTICIPANTS AND STUDY SETTING: Data from 39 undergraduate nursing students enrolled in a nursing healthcare ethics & law course using the ESA were recruited. ETHICAL CONSIDERATIONS: Participation was voluntary and informed without coercion. Written consent was obtained prior to participation. Research ethics approval was obtained by the Institutional Research Ethics Board of the recruited participants (Ethics # 2022-23-03). FINDINGS: The ESA provided structured self-reflection on the impact of shared vulnerability within a brave space. However, commitment to a brave space was not strongly influenced by the ESA, but rather by a mutual "commitment to others." CONCLUSION: A teaching tool such as an ESA can be used to facilitate instructor expectations of civil discourse and discussion of difficult topics. Rules of engagement such as those found in brave spaces can help transform fear of vulnerability into authentic growth for learners. A morally supportive learning space can support critical opportunities for ethical development. This study provides insight into how self-assessment and the use of a brave space in nursing ethics education can facilitate a morally supportive learning space.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".