The Effect of Augmented Feedback and Expertise on Spinal Manipulation Skills: An Experimental Study
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to investigate the combined effect of augmented feedback and expertise on the performance and retention of basic motor learning spinal manipulation skills. METHODS: A total of 103 chiropractic students with various training expertise were recruited for the study. Participants were evaluated at baseline, immediately after trials of augmented feedback practice and 1 week later. During all 3 assessments, students were asked to perform several trials of the same spinal manipulation, for which the maximum preload force, onset of thrust, thrust duration, force and peak force, thrust duration, rate of force application, and any drop in preload force were calculated. The constant error, absolute error, and variable error were calculated for the 3 experimental blocks of trials. RESULTS: Results confirmed that augmented feedback training modified several biomechanical parameters such as the rate of force application, the preload force, and the drop in preload force. The study also confirmed that many biomechanical parameters, including thrust duration and rate of force application, are modified with expertise but failed to identify any interaction effect between expertise and augmented feedback training effects. CONCLUSION: The study determined that expertise did not influence how students performed after a session of augmented feedback training. The study also determined that augmented feedback related to the global performance can yield improvements in several basic components of the spinal manipulation task. These results should be interpreted considering basic motor learning principles and specific learning environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".