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Record W2177885035 · doi:10.1002/nop2.32

Mary Seacole and claims of evidence‐based practice and global influence

2015· article· en· W2177885035 on OpenAlexaff
Lynn McDonald

Bibliographic record

VenueNursing Open · 2015
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNewspaperMemoirPromotion (chess)Health careHistoryNursingPolitical scienceMedia studiesMedicinePoliticsSociologyArt historyLaw

Abstract

fetched live from OpenAlex

Abstract Aim The aim of this paper was to explore the contribution of Mary Seacole to nursing and health care, notably in comparison with that of Florence Nightingale. Background Much information is available, in print and electronic, that presents Mary Seacole as a nurse, even as a pioneer nurse and leader in public health care. Her own memoir and copious primary sources, show rather than she was a businesswoman, who gave assistance during the Crimean War, mainly to officers. Florence Nightingale's role as the major founder of the nursing profession, a visionary of public health care and key player in advocating ‘environmental’ health, reflected in her own Notes on Nursing , is ignored or misconstrued. Design Discussion paper. Data sources British newspapers of 19th century and The Times digital archive; Australian and New Zealand newspaper archives, published memoirs, letters and biographies/autobiographies of Crimean War participants were the major sources. Results Careful examination of primary sources, notably digitized newspaper sources, British, Australian and New Zealand, show that the claims for Seacole's ‘global influence’ in nursing do not hold, while her use of ‘practice‐based evidence’ might better be called self‐assessment. Primary sources, moreover, show substantial evidence of Nightingale's contributions to nursing and health care, in Australia, New Zealand, the USA and many countries and the UK much material shows her influence also on hospital safety and health promotion.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.169
GPT teacher head0.447
Teacher spread0.277 · 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 designObservational
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

Citations6
Published2015
Admission routes1
Has abstractyes

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