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Record W1990120943 · doi:10.1016/j.jmpt.2005.01.002

Chiropractic in North America: A Descriptive Analysis

2005· article· en· W1990120943 on OpenAlexaboutno aff
Ian D. Coulter, Paul G Shekelle

Bibliographic record

VenueJournal of Manipulative and Physiological Therapeutics · 2005
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChiropracticMedicinePhysical therapyAlternative medicineFamily medicinePhysical medicine and rehabilitationPathology

Abstract

fetched live from OpenAlex

Objective This paper provides descriptive data on chiropractors, their practice, and their patients in North America in the past decade. Method Five sites in the United States and 1 in Canada were chosen, and a random sample of chiropractors was interviewed. In each practice, 10 patients were systematically selected on a single day. A total of 131 chiropractors and 1275 patients were interviewed. Summary The results suggest that doctors of chiropractic have firmly established themselves within the health care system in the United States and Canada and are able to attract patients who come to them directly for treatment, for largely back-related conditions, and who are willing to pay for their care. This paper provides descriptive data on chiropractors, their practice, and their patients in North America in the past decade. Five sites in the United States and 1 in Canada were chosen, and a random sample of chiropractors was interviewed. In each practice, 10 patients were systematically selected on a single day. A total of 131 chiropractors and 1275 patients were interviewed. The results suggest that doctors of chiropractic have firmly established themselves within the health care system in the United States and Canada and are able to attract patients who come to them directly for treatment, for largely back-related conditions, and who are willing to pay for their care.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.439
GPT teacher head0.409
Teacher spread0.030 · 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 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

Citations88
Published2005
Admission routes1
Has abstractyes

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