Learning Spinal Manipulation Skills: Assessment of Biomechanical Parameters in a 5-Year Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: Teaching spinal manipulation (SM) is a fundamental aspect of chiropractic training. Recent works have identified various biomechanical variables as indicators of SM performance and learning. However, only data from cross-sectional studies are available, limiting conclusions regarding the persistence of SM performance over the years. Therefore, the main objective of this investigation was to quantify the evolution of biomechanical parameters of SM over a 5-year learning period. METHODS: Thirty-three students enrolled in a chiropractic program participated in the present study. They were tested each year at the beginning of each fall semester by performing 10 SMs on an instrumented manikin while standing on a force plate. The procedure allowed us to measure various force-time parameters. RESULTS: Overall, significant time effects were noted for most dependent variables. The results indicated rapid improvement in the peak force applied and the rate of force production during the first 2 years. Time to peak force decreased drastically during the first year, whereas preload forces reached satisfactory levels during the third year. When various force-time requirements of bimanual task components were met, learners significantly reduced trial-to-trial variability of SM peak and preload forces, indicating automaticity of performance. Although global coordination improved in all learning processes, it was only in the later phase that learners reached values approaching those of experts. CONCLUSION: Overall, these results highlighted the importance of considering learning principles in the development of didactic strategies related to SM motor skills.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".