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Record W2131707620 · doi:10.1067/mmt.2000.106091

Motor learning and performance: A problem-based learning approach

2000· article· en· W2131707620 on OpenAlexaboutno aff
R.Kevin Pringle

Bibliographic record

VenueJournal of Manipulative and Physiological Therapeutics · 2000
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsChiropracticMedical educationMedicineReading (process)Problem-based learningAlternative medicineMathematics educationPsychology

Abstract

fetched live from OpenAlex

The way in which students of healthcare professions are learning basic and clinical sciences has been changing since the introduction of problem-based learning (PBL) at McMasters University (Toronto, Ontario) in 1974. 1 Mrozek JP. Problem solving facilitation in chiropractic education using the protable patient problem pack (P4). J Chiropr Educ. 1992; 6: 105-110 Google Scholar Chiropractic colleges have changed from a traditional, straightforward format to a PBL format since the inception of the Advantage Program at Los Angeles College of Chiropractic 2 Student handbook. Los Angeles College of Chiropractic. : LACC, Whittier (CA)1990 Google Scholar in 1990 and the Guided-Discovery program at National College of Chiropractic. 3 Student handbook. National College of Chiropractic. : National College of Chiropractic, Lombard (IL)1996 Google Scholar Despite these changes, the manner in which the motor skills components of chiropractic are taught is similar to teaching methods before PBL. This text describes ways to enhance the teaching of the art of chiropractic. This book should be part of faculty reading lists to bring the teaching of motor skills in chiropractic in line with the teaching of basic and clinical sciences.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.188
GPT teacher head0.365
Teacher spread0.177 · 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.

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

Citations245
Published2000
Admission routes1
Has abstractyes

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