Bibliographic record
Abstract
BACKGROUND: Nurses in the Western world have given considerable attention to the concept of vulnerability in recent decades. However, nurses have tended to view vulnerability from an individualistic perspective, and have rarely taken into account structural or collective dimensions of the concept. As the need grows for health workers to engage in the global health agenda, nurses must broaden earlier works on vulnerability, noting that conventional conceptualizations and practical applications on the notion of vulnerability warrant extension to include more collective conceptualizations thereby making a more complete understanding of vulnerability in nursing discourse. DISCUSSION: The purpose of this paper is to examine nursing contributions to the concept of vulnerability and consider how a broader perspective that includes socio-political dimensions may assist nurses to reach beyond the immediate milieu of the patient into the dominant social, political, and economic structures that produce and sustain vulnerability. SUMMARY: By broadening nurse's conceptualization of vulnerability, nurses can obtain the consciousness needed to move beyond a peripheral role of nursing that has been dominantly situated within institutional settings to contribute in the larger arena of social, economic, political and global affairs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.069 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".