Institutions of Care: A Qualitative Study with Ancestral Black Nova Scotian Nurses in Healthcare
Bibliographic record
Abstract
BackgroundAncestral Black Nova Scotian (ABNS) nurses are a culturally distinct group yet, little is known about their experiences. Available literature suggests that ABNS nurses are underrepresented in nursing and that they encounter discrimination throughout the health system. Understanding the experiences of ABNS nurses facilitates addressing antiBlack racism in nursing and healthcare.PurposeThis study sought to critically examine the leadership experiences of ABNS nurses in healthcare.MethodsThis qualitative study was guided by Black feminist theory and involved one-on-one semi-structured telephone interviews with eighteen ABNS nurses. Critical Discourse Analysis was applied in the reading of interview transcripts to examine words used by participants in relation to nursing and healthcare. The findings are presented in two conceptual themes.ResultsBlack Tax in Nursing captures the added physical, mental, and spiritual strain experienced by ABNS nurses navigating nursing and healthcare. Black Tax encompassed everyday microaggressions and systemic processes, including intra-profession tensions. Integrating into nursing was made increasingly difficult by a reinforcing network of gatekeepers, policies, and structural design. Nova Scotia Healthcare as an Archaic Institution depicts an antiquated "broken" paternalistic system that did not empower patients nor promote health. Additionally, nursing education was accused of reinforcing negative stereotypes, competency gaps, and mistrust with patients.ConclusionsInstitution of Care show how ABNS nurses challenge institutional standards and norms in their approach to nursing. ABNS nurses navigate nursing and the health system by maintaining a community-oriented approach to health. Addressing anti-Black racism in nursing and healthcare requires attention to multi-level processes within institutions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".