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Record W4387523294 · doi:10.2979/nashim.42.1.07

The Shaping of Military Nursing in Israel: 1947–1958

2023· article· en· W4387523294 on OpenAlexaboutno aff
Ronen Segev

Bibliographic record

VenueNashim A Journal of Jewish Women s Studies & Gender Issues · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperImmigrationHealth carePolitical scienceThe HolocaustNursingMilitary medicineMilitary personnelMedicineLaw

Abstract

fetched live from OpenAlex

Abstract: Unique realities influenced the development of the military nursing profession in Israel. While other countries, such as the United States, the United Kingdom and Canada, established military hospitals staffed by separately trained military nurses, conditions in Israel led to the development of interlocking military and civilian healthcare sectors, as the young country responded simultaneously to healthcare needs brought on by war, ongoing attacks on civilians, and massive waves of immigrants, including European Holocaust survivors and Jews from Arab countries. Relying on an analysis of documents in multiple archives, contemporaneous newspaper articles and interviews conducted with nurses who served in the 1948 Arab–Israeli War and the 1956 Sinai Campaign, this paper describes the development of the nursing profession in Israel through 1958, when military nursing was fully established as part of the civilian health sector, a reality that continues to the present.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.233
GPT teacher head0.501
Teacher spread0.267 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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