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Record W4415382217 · doi:10.1186/s12909-025-08052-3

Program standards and student competencies among global chiropractic accreditation agencies: a content analysis

2025· article· en· W4415382217 on OpenAlexaffabout
Claire Johnson, Bart N. Green, Lyndon Amorin-Woods, David Byfield, Waleska Crespo-Rivera, Philip Dewhurst, Chantale Doucet, Andy Dunn, Marina Fox, Amanda R Jones-Harris, Carolina Kolberg, Charmaine Korporaal, C.A. Little, D. Moore, John P. Mrozek, Gary D. Schultz, Gregory J. Snow, Stephney Whillier, David Wickes, Yi Kai Wong, Christopher Yelverton, Igor Himelfarb

Bibliographic record

VenueBMC Medical Education · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsCanadian Memorial Chiropractic CollegeCanadian Chiropractic AssociationUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsAccreditationContent analysisChiropracticCompetence (human resources)Protocol (science)Program evaluation

Abstract

fetched live from OpenAlex

BACKGROUND: Accreditation of healthcare provider training programs ensures graduate competency and provides a means for programs to improve. Accreditation consistency assures the public that healthcare providers have similar basic training across world regions. Currently, it is unknown if chiropractic accrediting agencies have congruent standards globally. Therefore, the purpose of this study was to investigate similarities and differences in student competencies and program standards among four chiropractic accreditation agencies worldwide. METHODS: A quantitative content analysis was performed on accreditation standards from regional international accreditation agencies responsible for accrediting the majority of the world's chiropractic degree programs. Agencies included the Council on Chiropractic Education (United States), the European Council on Chiropractic Education (Europe, United Kingdom, South Africa), the Council on Chiropractic Education Australasia (Australia, New Zealand, Malaysia), and the Council on Chiropractic Education Canada (Canada). The contents of the accrediting standards were coded using a standardized coding list. A modified Delphi technique was used by 21 international experts from December 1, 2023, to April 18, 2024. After four rounds of consideration to achieve consensus, the contents were analyzed for frequency and congruence of coded items across the accrediting agencies' standards. A two-way analysis of variance was conducted to identify if there were any differences among the accreditation agencies. RESULTS: = 0.87 for program standards; thus, the models' predictions align with the observed data. CONCLUSIONS: The study findings demonstrate congruence for student competencies and program standards among chiropractic accreditation agencies across multiple geographic regions. The patterns of content were stable and consistent across the four accrediting agencies, with no evidence of differential effects among the agencies. In addition, this study provides essential details and standardized codes for agencies' documents, which may facilitate dialogue and comprehension among agencies, educators, regulators, governing officials, and other stakeholders in chiropractic education. STUDY REGISTRATION: The study protocol was prospectively registered with Open Science Framework on November 30, 2023 https://doi.org/10.17605/OSF.IO/259WC .

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)0.017
metaresearch head score (Gemma)0.042
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 score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.006
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.001
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 head0.036
GPT teacher head0.435
Teacher spread0.398 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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