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Record W3123948237 · doi:10.1017/jbr.2020.191

Presidential Address: The 1890s Debate over the Democratic Control of Hospitals in Britain and New Zealand

2021· article· en· W3123948237 on OpenAlexaboutno aff
Anna Clark

Bibliographic record

VenueJournal of British Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperDemocracyPresidential systemHealth carePolitical scienceHistoryEconomic historyLawPolitics

Abstract

fetched live from OpenAlex

Abstract Anna Clark's presidential plenary to the 2018 North American Conference on British Studies in Vancouver, British Columbia, compares scandals over the mistreatment of patients and nurses that led to demands for popular control of hospitals in both Britain and New Zealand in the 1890s. A high death rate at the Chelsea Hospital for Women in London, located near a Pasteur Institute for animal research on vaccination, incited fears of human vivisection. The high death rate of nurses at the London Hospital provoked newspaper exposés and parliamentary investigations and calls for the municipalization of voluntary hospitals. In Christchurch, New Zealand, a debate over the rudeness of doctors and nurses enraged citizens. The flames of these scandals were sparked by newspaper agitation but fanned by feminists, socialists, trade unionists, and animal-rights organizations. In response to fears around experimentation, Fabian socialists Havelock Ellis, Harry Roberts, and Honnor Morten proposed democratic control of hospitals. These demands, focusing on patients’ rights and nurses’ health, differed from the hospital reform movement that urged hospitals to become more economical by forcing patients to pay. They also diverged from Beatrice and Sidney Webb's admonitions that the state must oversee citizens’ health for the nation to function efficiently. Although the calls for the democratic control of hospitals did not succeed, they might be seen as germs of a patient-centered approach to hospital care.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.023
GPT teacher head0.329
Teacher spread0.306 · 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 designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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