Effects of Visual Feedback on Manipulation Performance and Patient Ratings
Bibliographic record
Abstract
OBJECTIVE: This study examined the explicit targeted outcome (a criterion standard) and visual feedback on the immediate change in and the short-term retention of performance by novice operators for a high-velocity, low-amplitude procedure under realistic conditions. METHODS: This study used a single-blind randomized experimental design. Forty healthy male (n = 26) and female (n = 14) chiropractic student volunteers with no formal training in spinal manipulative therapy participated. Biomechanical parameters of an L4 mammillary push spinal manipulation procedure performed by novice operators were quantified. Participants were randomly assigned to 2 groups and paired. One group received visual feedback from load-time histories of their performance compared with a criterion standard before a repeat performance. Participants then performed a 10-minute distractive exercise consisting of National Board of Chiropractic Examiners review questions. The second group received no feedback. An independent rating of performance was conducted for each participant by his/her partner. Results were analyzed separately for biomechanical parameters for partner ratings using the Student t test with levels of significance (P < .01) adjusted for repeated testing. RESULTS: Expressed in percent change for each individual, visual feedback was associated with change in the biomechanical performance of group 2, a minimum of 14% and a maximum of 32%. Statistical analysis rating of the performance favored the feedback group on 4 of the parameters (fast, P < .0008; force, P < .0056; precision, P < .0034; and composite, P < .0016). CONCLUSION: Quantitative feedback, based on a tangible conceptualization of the target performance, resulted in immediate and significant improvement in all measured parameters. Newly developed skills were retained at least over short intervals even after distractive tasks. Learning what to do with feedback on one's own performance may be more important than the classic teaching of how to do it.
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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 | 0.034 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".