Can self-assessment and augmented feedback improve performance and learning retention in manual therapy: results from an experimental study
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to investigate how feedback and self-assessment strategies affect performance and retention of manual skills in a group of chiropractic students. METHODS: Seventy-five students participated in two spinal manipulation (SM) learning sessions using a force-sensing table. They were recruited between May and November 2022 during HVLA technical courses. Students were randomly assigned into three different groups: participants in group 1 received visual feedback, those in group 2 received visual feedback after self-assessment, and participants in group 3 (C) received no feedback. During the first session, participants started with one block of 3 familiarization trials, followed by two blocks of 6 SM HVLA (high velocity low amplitude) posterior-to-anterior thoracic SM trials, with 3 trials performed with a target force of 450 N and 3 others at 800 N. They received feedback according to their group during the first block, but no feedback was provided during the second block. All participants were invited to participate in a second session for the retention test and to perform a new set SM without any form of feedback. RESULTS: Results showed that visual feedback and visual feedback in addition to self-assessment did not improve short-term SM performance, nor did it improve performance at the one-week retention test. The group that received visual feedback and submitted to self-assessment increased the difference between the target force and the peak force applied, which can be considered a decrease in performance. CONCLUSION: No learning effects between the three groups of students exposed to different feedback and self-assessment learning strategies were highlighted in the present study. However, future research on innovative motor learning strategies could explore the role of external focus of attention, self-motivation and autonomy in SM performance 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".