Learning Spinal Manipulation: The Effect of Expertise on Transfer Capability
Bibliographic record
Abstract
OBJECTIVE: Transfer capability represents the changes in performance in one task that result from practice or experience in other related tasks. Increased transfer capability has been associated with expertise in several motor tasks. The purpose of this study was to investigate if expertise in spinal manipulation therapy, assessed in groups of trainees and experienced chiropractors, is associated with increased transfer capabilities. METHODS: Forty-nine chiropractic students (fifth- and sixth-year students) and experienced chiropractors were asked to perform blocks of 10 thoracic spine manipulations in 3 different conditions: preferred position and table setting, increased table height, and unstable support surface. Spinal manipulations were performed on a computer-connected device developed to emulate a prone thoracic spine manipulation. Thrust duration, thrust force rate of force application, and preload force were obtained for each trial and compared across groups and conditions. RESULTS: Results indicated that both expertise and performance conditions modulated the biomechanical parameters of spinal manipulation. Decreased thrust duration and increased rate of force application were observed in experienced clinicians, whereas thrust force and thrust rate of force application were significantly decreased when task difficulty was increased. Increasing task difficulty also led to significant increases in performance variability. CONCLUSION: Overall, this study suggests that when instructed to perform spinal manipulation in a challenging context, trainees and experts choose to modulate force to optimize thrust duration, a characteristic feature of high-velocity, low-amplitude spinal manipulation. Given its known association with motor proficiency, transfer capability assessments should be considered in spinal manipulative therapy training.
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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 | 0.001 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".