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Record W1965108805 · doi:10.1054/homp.1999.0376

Improving the evaluation of homeopathy: economic considerations and impact on health

2000· article· en· W1965108805 on OpenAlexaboutno aff
G Chaufferin

Bibliographic record

VenueBritish Homeopathic journal · 2000
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHomeopathyReimbursementMedical prescriptionActuarial scienceQuarter (Canadian coin)Public healthEconomic evaluationHealth careMedicineScope (computer science)Quality (philosophy)BusinessPublic economicsAlternative medicineEconomicsEconomic growthNursingGeography

Abstract

fetched live from OpenAlex

OBJECTIVES: The financial crisis of health insurance systems sometimes drives public policy-makers to take precipitate action dominated by economic imperatives. The question addressed here consists in defining homeopathy's scope of intervention, its place in health care strategies, recourse to treatment, and especially economic data appraising homeopathy's impact on expenditures and outlay covered by health insurance in France. METHOD: We used the General Evaluation Model to define the study (to whom is the evaluation made, situations, criteria, measurement of these criteria, quality and precision). RESULTS: The main results are in terms of costs, as follows: * For reimbursable medicines the public sales price of homeopathic products is a quarter of the average. * The total reimbursement for a prescription of allopathic products is three times more than for a prescription of homeopathic products. * Homeopathic physicians incur annual reimbursement outlays which are half those of general practitioners. The differences observed cannot be explained by the patient profile or the diseases treated. Furthermore, a study carried out in France showed that 87% of patients whose physicians had prescribed homeopathic treatment did not see another physician for the same problem. CONCLUSIONS: Once basic analysis has been done, so that the economic and epidemiological impact of a certain health strategy can be determined, it is necessary to proceed further, helping healthcare providers and public authorities to make decisions. Various possibilities exist and should be investigated.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0020.000

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 head0.053
GPT teacher head0.363
Teacher spread0.310 · 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.

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

Citations17
Published2000
Admission routes1
Has abstractyes

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