Learning Spinal Manipulation
Bibliographic record
Abstract
PURPOSE: The goal of the present study was to quantify the high-velocity, low-amplitude spinal manipulation biomechanical parameters in two cohorts of students from different teaching institutions. The first cohort of students was taught chiropractic techniques in a patient-doctor positioning practice setting, while the second cohort of students was taught in a "complete practice" manipulation setting, thus performing spinal manipulation skills on fellow student colleagues. It was hypothesized that the students exposed to complete practice would perform the standardized spinal manipulation with better biomechanical parameters. METHODS: Participants (n = 88) were students enrolled in two distinct chiropractic programs. Thoracic spine manipulation skills were assessed using an instrumented manikin, which allowed the measurement of applied force. Dependent variables included peak force, time to peak force, rate of force production, peak force variability, and global coordination. RESULTS: The results revealed that students exposed to complete practice demonstrated lower time to peak force values, higher peak force, and a steeper rate of force production compared with students in the patient-doctor positioning scenario. A significant group by gender interaction was also noted for the time to peak force and rate of force production variables. CONCLUSION: The results of the present study confirm the importance of chiropractic technique curriculum and perhaps gender in spinal manipulation skill learning. It also stresses the importance of integrating spinal manipulation skills practice early in training to maximize the number and the quality of significant learner-instructor interactions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".