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Reforming hospital nursing: the experiences of Maria Machin

2006· article· en· W1971288358 on OpenAlexafffundabout
Carol Helmstadter

Bibliographic record

VenueNursing Inquiry · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity of Toronto
FundersAssociated Medical ServicesWellcome Trust
KeywordsNursingTeam nursingService (business)Quarter (Canadian coin)Nursing careNurse educationPrimary nursingNursing researchMedicineBusinessHistory

Abstract

fetched live from OpenAlex

The reform of hospital nursing in the last quarter of the nineteenth century brought nursing leaders into conflict with the gendered and class bound structure of Victorian society. The experiences of Maria Machin are used in this article as an example of the barriers nursing leaders had to overcome in order to establish a competent nursing service. While Machin was eminently successful in improving patient care and expanding the knowledge base of her nurses, she could not change the perceptions of nursing which the public at large held. At the beginning of the nineteenth century hospital nurses had been essentially cleaning women who gave some of the less important nursing care. They formed a cheap service which many hospital governors considered a relatively low priority in the overall operation of the hospital. This view of nursing persisted long after the reformers had made nursing into something quite different. Machin's nursing career also illustrates how nursing participated in a major aspect of British imperialism, the export of professional expertise and administrative skills as well as the way nursing fitted into the rise of the new professionalism.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.299
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2006
Admission routes3
Has abstractyes

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