Nurses, the Oppressed Oppressors: A Qualitative Study
Bibliographic record
Abstract
Healthcare equity, defined as rightful and fair care provision, is a key objective in all health systems. Nurses commonly experience cases of equity/inequity when caring for patients. The present study was the first to explain nurses' experience of equal care. A qualitative study sought to describe the experiences of 18 clinical nurses and nurse managers who were selected through purposive sampling. The inclusion criteria were the nurses' familiarity with the subject of the study and willingness to participate. The data were collected through in-depth, unstructured, face-to-face interviews. The sampling continued up to data saturation. All the interviews were recorded and then transcribed word by word. The data were analyzed using thematic analysis. The major theme extracted in this study was the equation between submissiveness and oppression in nurses. It had two subthemes, namely the oppressed nurse and the oppressive nurse. The first subtheme comprised three categories including nurses' occupational dissatisfaction, discrimination between nursing personnel, and favoring physicians over nurses. The second subtheme consisted of three categories, namely habit-oriented care provision, inappropriate care delegation, and care rationing while neglecting patient needs. When equal care provision was concerned, the participating nurses fluctuated between states of oppression and submissiveness. Hence, equal conditions for nurses are essential to equal care provision. In fact, fair behavior toward nurses would lead to equity nursing care provision and increase satisfaction with the healthcare system.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".