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Record W2316152251 · doi:10.1177/2156587215604073

Attitudes Toward Chiropractic

2015· article· en· W2316152251 on OpenAlexaffabout
Carol Ann Weis, Kent Stuber, Jon Barrett, Alexandra Greco, Alexander Kipershlak, Tierney Glenn, Ryan Desjardins, Jennifer Nash, Jason W. Busse

Bibliographic record

VenueJournal of Evidence-Based Complementary & Alternative Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreMcMaster UniversityCanadian Memorial Chiropractic College
Fundersnot available
KeywordsChiropracticPsychologyMedicineAlternative medicine

Abstract

fetched live from OpenAlex

We assessed the attitudes of Canadian obstetricians toward chiropractic with a 38-item cross-sectional survey. Ninety-one obstetricians completed the survey, for a response rate of 14% (91 of 659). Overall, 30% of respondents held positive views toward chiropractic, 37% were neutral, and 33% reported negative views. Most (77%) reported that chiropractic care was effective for some musculoskeletal complaints, but 74% disagreed that chiropractic had a role in treatment of non-musculoskeletal conditions. Forty percent of respondents referred at least some patients for chiropractic care each year, and 56% were interested in learning more about chiropractic care. Written comments from respondents revealed concerns regarding safety of spinal manipulation and variability among chiropractors. Canadian obstetricians' attitudes toward chiropractic are diverse and referrals to chiropractic care for their patients who suffer from pregnancy-related low back pain are limited. Improved interprofessional relations may help optimize care of pregnant patients suffering from low back pain.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.699
Threshold uncertainty score0.606

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.627
GPT teacher head0.508
Teacher spread0.119 · 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 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

Citations15
Published2015
Admission routes2
Has abstractyes

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