Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".