Bibliographic record
Abstract
The extension of blood transfusion to civilian populations was contingent on the availability of a nursing workforce capable of taking on increasingly responsible roles. Nurses assumed a variety of roles as they incorporated blood work into patient care and, in the process, enabled, embodied, and engendered it as nurses' and women's work. Initially, the student workforce facilitated transfusion through roles that were congruent with nursing's domestic roots. Later, it constrained the expansion of blood work because of its perpetually novice nature. Delegation constituted one strategy by which a limited number of persons could become experienced and autonomous in a particular role. As long as the skill remained limited, nurses shared its associated power and status, which differentiated them within the work culture. A few women were able to shape blood work to their advantage, using their expertise either as job security or as a bargaining point to negotiate better working conditions. However, when the skill was routinized and dispersed among many nurses, it became dirty work. The examination of one specific technology that shifted from medicine into nursing contributes insights to current issues of expanded roles and delegated skills. Nurses need to question seriously what is gained and lost as they take on and let go of technologies. They need to consider what kinds of knowledge will be needed and how best to develop it. Finally, they need to reflect how changes might complicate care giving and nurses' work.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".