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Record W3150695153 · doi:10.1515/jcim-2020-0187

A spatial-temporal study of complementary and alternative medicine (CAM) by type: exploring localization economies implications in urban areas in Ontario

2021· article· en· W3150695153 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Complementary and Integrative Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCluster analysisSpatial analysisGeographyEconomic geographyCommon spatial patternRegional scienceCartographyData miningData scienceComputer scienceArtificial intelligenceEcologyBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: This study adds to the geography of complementary and alternative medicine (CAM) literature by comparing the spatial-temporal patterns of five types of CAM within 19 cities in light of clustering benefits from localization economies. METHODS: CAM office location points and nearest neighbour, standard distance, local spatial autocorrelation, and Mann-Whitney analyses are utilized to test potential clustering tendencies of CAM types over time. RESULTS: It is shown that 'within' (chiropractors near chiropractors, for example) and 'amongst' (chiropractors proximate to other CAM types) spatial clustering occurs in 2007 and 2017. This implies the persistent influence of localization economies. CONCLUSIONS: Continued clustering of CAM within urban locations already replete with CAM offices will widen spatial disparities through time. This has implications for policy-makers concerned with dispersing medical resources over space for better accessibility.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.110
GPT teacher head0.349
Teacher spread0.239 · 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