MétaCan
Menu
Back to cohort
Record W2417048151

Self-reported attitudes, skills and use of evidence-based practice among Canadian doctors of chiropractic: a national survey.

2015· article· en· W2417048151 on OpenAlexaffabout
André Bussières, Lauren Terhorst, Matthew Leach, Kent Stuber, Roni Evans, Michael Schneider

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsCanadian Memorial Chiropractic CollegeUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsChiropracticMedical educationEvidence-based practiceClinical PracticePsychologyFamily medicineMedicineAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To identify Canadian chiropractors' attitudes, skills and use of evidence based practice (EBP), as well as their level of awareness of previously published chiropractic clinical practice guidelines (CPGs). METHODS: 7,200 members of the Canadian Chiropractic Association were invited by e-mail to complete an online version of the Evidence Based practice Attitude & utilisation SurvEy (EBASE); a valid and reliable measure of participant attitudes, skills and use of EBP. RESULTS: Questionnaires were completed by 554 respondents. Most respondents (>75%) held positive attitudes toward EBP. Over half indicated a high level of self-reported skills in EBP, and over 90% expressed an interest in improving these skills. A majority of respondents (65%) reported over half of their practice was based on evidence from clinical research, and only half (52%) agreed that chiropractic CPGs significantly impacted on their practice. CONCLUSIONS: While most Canadian chiropractors held positive attitudes towards EBP, believed EBP was useful, and were interested in improving their skills in EBP, many did not use research evidence or CPGs to guide clinical decision making. Our findings should be interpreted cautiously due to the low response rate.

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)0.013
metaresearch head score (Gemma)0.095
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.095
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.537
GPT teacher head0.510
Teacher spread0.027 · 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.

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

Citations62
Published2015
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicHealth Sciences Research and EducationFrench-language works237,207