Systematic Augmented Feedback and Dependency in Spinal Manipulation Learning: a Randomized Comparative Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of the study was to evaluate if systematic augmented feedback during short sessions of spinal manipulation (SM) training creates a dependency compared with short training session characterized by progressive withdrawal of augmented feedback. METHODS: Forty fourth- and fifth-year chiropractic students enrolled in a 5-year chiropractic program were randomized into 2 groups. The 2 groups performed the same number of SM with a 300-N peak force target on an instrumented device. Baseline assessment consisted of 10 trials without feedback. Three training blocks of 10 SMs were then performed with visual and verbal feedback. For the control group, feedback was always provided. For the experimental group, augmented feedback was provided for each trial of the first training block, 50% of the second block, and 20% of the last training block. A postintervention assessment of 10 trials without feedback was performed, and a retention assessment was conducted 20 minutes later. RESULTS: No group main effect was found on biomechanical parameters and error variables. A main effect of learning for the absolute error was observed, suggesting that short sessions of feedback training improve participants' accuracy. CONCLUSION: The results of the study suggest that feedback scheduling does not influence SM motor performance and learning in clinically experienced students.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".