A ‘quiet’ crisis in health care: developing our capacity to hear
Bibliographic record
Abstract
Abstract In this paper we suggest that it is a mistake to understand the current situation in nursing as primarily a problem of ‘shortage’, a problem which may be solved through supplying ‘more nurses, faster’. This way of thinking is understood as reflecting an invalidation of nurses and nursing and, by fostering false beliefs about nursing, as functioning to exacerbate rather than resolve the current situation. Unlike many mainstream conceptualizations of the current situation in nursing, we begin by understanding the experiences and concerns of nurses as meaningful. Feminist and hermeneutic philosophies, as well as Foucauldian perspectives on discourse, encourage us to take seriously the gender relations of power through which nursing comes to be articulated and to cultivate ways of thinking which can generate more productive analyses of the current situation in nursing. Rather than accepting instrumental understandings of nursing as adequate, we question the everyday beliefs and assumptions, the dominant discourses at work both in the world and in ourselves, which allow the suffering of nurses to be thought irrelevant and their concerns to remain unheard. We theorize both why the suffering of nurses can be considered irrelevant in this way and the difference it would make were we to take the experiences of nurses seriously. This undertaking requires that we reflect on that which constrains – and enables – the ways we are able to write, speak and think about nursing.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".