Bibliographic record
Abstract
BACKGROUND: Nurses' inter- and intra-disciplinary relationships are frequently interpreted as oppressed group behaviours, contributing to their relatively dis-empowered status. In the context of restructuring in health care, this study examined the views of hospital staff nurses about their relationships with nursing colleagues and other health care professionals and their ideas for change. AIM: The aim of this paper is to report a study to add the views of staff nurses to the discourse on restructuring and to make visible the processes that contribute to their marginalization. DESIGN: The study was descriptive and exploratory. Staff nurses from various hospitals in a large Canadian city were selected by theoretical sampling. METHOD: Twenty volunteer staff nurses were interviewed between 1998 and 1999, using broad, open-ended questions and prompts to explore nurses' various relationships in the health care system. This approach allowed for multiple responses and expansions of ideas, without losing focus. The interviews were audio-taped and later transcribed. Thematic analysis was carried out. FINDINGS: Many participants were aware of inter-disciplinary hierarchies, particularly between nurses and physicians. Many also showed insights into their own intra-disciplinary hierarchies and mutual non-supportiveness. Both types of relationships were found to be inextricably linked, sustaining nurses' oppression through dis-empowering discourses. Nurses expressed many ideas about how to promote mutually supportive relationships. CONCLUSION: Change for the better needs to come from within the nursing profession. To develop effective strategies, bedside nurses have to be included in decision-making processes affecting them and their practice, about which they are the experts. Mutual respect, awareness-raising through education, development of caring nursing communities, mentorship and non-hierarchical leadership are key to stopping dis-empowering discourses and practices amongst nurses.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".