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Record W2808792897 · doi:10.1186/s12998-018-0194-y

Chiropractic in Global Health and wellbeing: a white paper describing the public health agenda of the World Federation of Chiropractic

2018· article· en· W2808792897 on OpenAlexaff
Michele Maiers, Mustafa Agaoglu, Richard A. Brown, Christopher Cassirer, Kendrah DaSilva, Reidar P. Lystad, Sarkaw Mohammad, Jessica J. Wong

Bibliographic record

VenueChiropractic & Manual Therapies · 2018
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCanadian Memorial Chiropractic CollegeCanadian Chiropractic Association
Fundersnot available
KeywordsChiropracticMedicinePublic healthHealth careHealth promotionWhite paperAlternative medicinePublic relationsNursingPolitical sciencePathology

Abstract

fetched live from OpenAlex

The World Federation of Chiropractic supports the involvement of chiropractors in public health initiatives, particularly as it relates to musculoskeletal health. Three topics within public health have been identified that call for a renewed professional focus. These include healthy ageing; opioid misuse; and women's, children's, and adolescents' health. The World Federation of Chiropractic aims to enable chiropractors to proactively participate in health promotion and prevention activities in these areas, through information dissemination and coordinated partnerships. Importantly, this work will align the chiropractic profession with the priorities of the World Health Organization. Successful engagement will support the role of chiropractors as valued partners within the broader healthcare system and contribute to the health and wellbeing of the communities they serve.

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)0.019
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0100.011
Scholarly communication0.0170.012
Open science0.0020.011
Research integrity0.0210.020
Insufficient payload (model declined to judge)0.0060.002

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 head0.067
GPT teacher head0.354
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations18
Published2018
Admission routes1
Has abstractyes

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