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Record W2612422908

The use of natural health products (NHP) by Ontario seniors

2001· article· en· W2612422908 on OpenAlexaffabout
Nicole C. Brazier, Michel Bédard, Kevin Brazil, Kathryn Gaebel, Marissa J. Levine, L. Lohfield, S. MacLeod

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2001
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNatural (archaeology)Environmental healthMedicineGerontologyGeography
DOInot available

Abstract

fetched live from OpenAlex

A random dialing telephone survey in 4 Ontario communities obtained data on the use of natural health products (NHP) from 1,071 persons 60 years and older. 553 (52%) respondents were users of NHP. Prevalence of use was similar for females (53%) and males (48%). In this population modal users were of European descent, high school graduates and employed at least part-time. Half the users received recommendations about NHP from friends or relatives; another 22% learned about NHP through self-experimentation. Most users (81 %) decided by themselves whether they would buy an NHP rather than rely on input from another source (herbalist, physician, store owner/employee). 38% of NHP users had not informed their physician that they were using an NHP. When users had discussed NHP with their physician, less than 5% of physicians responded negatively. Some users felt natural health products were safer (15%) and less expensive (4%) than prescription drugs. 30% used NHP as a last resort for the treatment of a chronic disease. Nearly half (49%) of the users believed that if the government pays for prescription drugs, it should also pay for herbal remedies; 36% said the consumer should pay. In light of the extensive use of NHP by seniors, there is a need for clinical pharmacology studies of these products.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.340
Teacher spread0.270 · 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 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

Citations3
Published2001
Admission routes2
Has abstractyes

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