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Record W2000720803 · doi:10.1155/2012/647896

The Flexner Report of 1910 and Its Impact on Complementary and Alternative Medicine and Psychiatry in North America in the 20th Century

2012· article· en· W2000720803 on OpenAlexafffundabout
Frank W. Stahnisch, Marja J. Verhoef

Bibliographic record

VenueEvidence-based Complementary and Alternative Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsBiomedicineAlternative medicineFoundation (evidence)RestructuringCommissionPsychiatryPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

America experienced a genuinely vast development of biomedical science in the early decades of the twentieth century, which in turn impacted the community of academic psychiatry and changed the way in which clinical and basic research approaches in psychiatry were conceptualized. This development was largely based on the restructuring of research universities in both of the USA and Canada following the influential report of Johns Hopkins-trained science administrator and politician Abraham Flexner (1866-1959). Flexner's report written in commission for the Carnegie Foundation for the Advancement of Teaching in Washington, DC, also had a major influence on complementary and alternative medicine (CAM) in psychiatry throughout the 20th century. This paper explores the lasting impact of Flexner's research published on modern medicine and particularly on what he interpreted as the various forms of health care and psychiatric treatment that appeared to compete with the paradigm of biomedicine. We will particularly draw attention to the serious effects of the closing of so many CAM-oriented hospitals, colleges, and medical teaching programs following to the publication of the Flexner Report in 1910.

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)0.008
metaresearch head score (Gemma)0.029
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.012
Scholarly communication0.0060.008
Open science0.0010.004
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.095
GPT teacher head0.399
Teacher spread0.304 · 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 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

Citations55
Published2012
Admission routes3
Has abstractyes

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