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Record W2895655177 · doi:10.1142/s2575900018200033

Exploring Traditional Chinese Medicine in Alberta: Challenges and opportunities

2018· article· en· W2895655177 on OpenAlexaffabout
Vesna Nguyen, Janice M. Leung, Richard Lewanczuk, Sunita Vohra, Carl Amrhein

Bibliographic record

VenueTraditional Medicine and Modern Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsAlberta Health ServicesUniversity of AlbertaAlberta HealthUniversity of Saskatchewan
FundersFudan University
KeywordsBusinessPolitical sciencePublic relationsEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

Integrative medicine is commonplace across the world, but in North America, it is considered a complement, rather than a mainstay of health care delivery. In Canada, where conventional Western medicine dominates modern health practices, we explore the progress, challenges, and opportunities of complementary medical practices, in particular Traditional Chinese Medicine (TCM) in the province of Alberta. We provide a TCM policy framework and maturity model as tools to assess the overall state of TCM practices and apply them in an Albertan context. While Alberta has made significant progress in developing capacity, competence, and accountability within TCM practices, the maturity of its practices may be considered to be in their infancy compared to more developed Chinese jurisdictions and some other Canadian provinces. We highlight significant gaps and barriers that limit the potential for complementary medicine to become part of mainstream health care as safe, effective, and quality health care choices, and discuss possible next steps.

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)0.004
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.628

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0090.005
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.617
GPT teacher head0.366
Teacher spread0.251 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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