MétaCan
Menu
Back to cohort
Record W4321613015

Identifying and prioritizing research to inform a research agenda for Canadian chiropractors working in sport - the Canadian sports chiropractic perspective.

2022· article· en· W4321613015 on OpenAlexaffabout
Alexander Dennis Lee, Lara deGraauw, Brad Muir, Melissa Belchos, David Oh, Kaitlyn Szabo, Kent Murnaghan, Chris deGraauw, Scott Howitt

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsDiscovery Air (Canada)Etobicoke School of the ArtsCentre for Disability Prevention and RehabilitationOntario Tech UniversityEtobicoke General HospitalCanadian Memorial Chiropractic College
Fundersnot available
KeywordsChiropracticDelphi methodPolitical sciencePsychological interventionHealth careLibrary scienceMedicineNursingAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Objectives: To identify and prioritize research to inform research agenda development for Canadian chiropractors working in sport. Methods: Clinicians, researchers and leaders from the Canadian sports chiropractic field were invited to participate in 1) a survey to refine a list of research priorities, 2) a Delphi procedure to determine consensus on these priorities, and 3) a prioritization survey. Results: The top three research priorities were 1) effects of interventions on athletic outcomes, 2) research about sports healthcare teams, and 3) clinical research related to spinal manipulative and mobilization therapy. The three highest ranked conditions to research were 1) low back pain, 2) neck pain, and 3) concussion. Collaborations with sports physicians and universities/ colleges were rated as important research collaborations to pursue. Conclusions: These results represent the Canadian sports chiropractic perspective to research priority setting and will be used alongside stakeholder input to set the first research agenda for the Canadian sports chiropractic field.

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)0.051
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0090.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.461
GPT teacher head0.506
Teacher spread0.045 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicDelphi Technique in ResearchFrench-language works237,207