The Prevailing Winds of Oppression: Understanding the New Graduate Experience in Acute Care
Bibliographic record
Abstract
TOPIC: The experience of new graduates in acute care. PURPOSE: The majority of newly graduated nurses make their initial professional role transition in acute care. Being socialized into the dynamic culture of today's hospitals creates significant challenges not only for the nurses themselves but also for institutions of higher education, healthcare administrators, and policy makers across this country. Demanding workloads for hospital nurses, an aging nursing workforce, and the high level of stress inherent in workplaces across North America are factors contributing to an exodus of both new and seasoned nurses out of acute care. This article outlines the implicit and explicit factors that may be contributing to the dissatisfaction and distress in nursing graduates entering professional practice through hospital nursing. SOURCES OF INFORMATION: CINAHL, MEDLINE, Sociolit, and PubMed. CONCLUSION: Discussion is focused on the oppressive context in which hospital nursing continues to be situated and explores the ideological, structural, and relational aspects of domination that continue to surface in the work experiences of novice as well as seasoned nurses. Suggestions for addressing the issues that plague the acute care environment are integrated throughout the article, and a detailed framework of empowerment for this nursing context is offered.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".