Do we perform as well as we think we do? A systematic scoping review of self-evaluation of upper-extremity motor performance
Bibliographic record
Abstract
The ability to self-evaluate motor performance or estimate performance errors is beneficial for motor learning or relearning in the context of neurologic injury. Some evidence suggests those with injury like stroke may be unable to accurately self-evaluate their performance; however, it is unclear if individuals who are absent of injury are accurate in this domain. We aimed to investigate the accuracy of self-evaluation and potential influencing factors by conducting a systematic search to identify literature involving the self- and objective-evaluation of upper-extremity motor tasks. Twenty-three studies satisfied inclusion criteria. Data revealed a moderate positive correlation between self- and objective evaluations across a variety of tasks, from trivial button pressing to specialized surgical suturing. Both under- and overestimation of performance was found across the papers. Key factors identified to influence the accuracy of self-evaluation were the task purpose, familiarity, difficulty, and whether an individual received a demonstration. This review identified some limitations in this field of research. Most notably, we found that very few studies have investigated the accuracy of self-evaluation of motor performance with the primary goal of comparison to objective performance. Many studies reported the data but did not make direct statistical comparisons. Moreover, due to inconsistencies between how self and objective-evaluations were conducted, we argue that in this area of investigation self-evaluation tools need to replicate the objective evaluation method, or at minimum the self-evaluation tool should ask questions specific to the construct of performance that is being measured objectively.
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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 | 0.035 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".