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Record W2034815475 · doi:10.1136/ebn.4.3.68

Florence Nightingale and the early origins of evidence-based nursing

2001· article· en· W2034815475 on OpenAlexaff
Lynn McDonald

Bibliographic record

VenueEvidence-Based Nursing · 2001
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHistoryNursingMedicine

Abstract

fetched live from OpenAlex

Florence Nightingale and the early origins of evidence-based nursingFlorence Nightingale is now a much ignored historical figure.The publication of the Collected Works of Florence Nightingale by Wilfred Laurier University Press, beginning in 2001, will outline her enormous contribution not only to the foundation of the nursing profession but also to the establishment of a public healthcare system.The Collected Works will include her published works and many unpublished letters and notes.Not the least of the areas where quite a new Nightingale will appear is in evidence-based nursing, a term that was not in use in her day, but a concept central to her own theory of nursing and health care. A passionate statisticianLet's begin by looking at Nightingale as a systemic thinker and a "passionate statistician."Her work in nursing and social reform was informed by a religious faith or philosophy that favoured a systemic approach: God made the world and runs it by laws, which we can discover by research in both the biophysical and social spheres.For Nightingale, this entailed the best possible research, access to the best available government statistics and expertise, and the collection of new material where the existing stock was inadequate.Nightingale's leadership style was very much knowledge based.She herself was a pioneer developer of survey instruments, always vetted by other experts and pretested on appropriate cases.She was also a pioneer in the graphical presentation of data.At a time when research reports were only beginning to include tables, Nightingale was using bar and pie charts, which were colour coded to highlight key points (eg, high mortality rates under certain conditions).Nightingale was keen not only to get the science right but also to make it comprehensible to lay people, especially the politicians and senior civil servants who made and administered the laws. Evidence-based practiceSeveral examples clearly reflect an evidence-based framework, ranging from Nightingale's first work after her return as a heroine from the Crimean War in 1856, to a late attempt to influence social policy with a proposal for a chair in "social physics" at Oxford University in 1891.These examples will be described briefly.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0070.037
Scholarly communication0.0100.013
Open science0.0020.006
Research integrity0.0080.021
Insufficient payload (model declined to judge)0.0080.002

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 head0.109
GPT teacher head0.361
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations102
Published2001
Admission routes1
Has abstractyes

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