Knowing When to Look It Up: A New Conception of Self-Assessment Ability
Bibliographic record
Abstract
BACKGROUND: Although self-assessment is widely acknowledged as a vital skill for members of self-regulating professions, a ubiquitous finding in the research literature is that self-ratings are quite poor when compared with externally generated measures of ability. Many researchers have identified this as a serious problem for the concept of self-regulation in the professions. However, we question the sufficiency of the operational definitions of self-assessment on which the previous research is based. This study examines the validity of a new conceptualization of self-assessment in practice and evaluates a series of measures for capturing self-assessment ability as defined by this new conceptualization. METHOD: Using a computer-delivered free-response test, the authors generated three measures intended to capture situational awareness: (1) response times to questions, (2) the ability to avoid responding to questions for which the respondent is less likely to be correct, and (3) the ability to select questions from content areas in which respondents have greater ability. In addition, the traditional measures of self-assessment (e.g., predictions of how many questions one would answer correctly) were administered. RESULTS: Participants showed behavioral indications of being aware of the limits of their ability. They took longer to respond when their eventual answer was incorrect relative to when it was correct, they were able to avoid answering questions on which they were likely to be incorrect, and they selected content-based domains in an appropriate order given their accuracy. DISCUSSION: These results provide evidence in favor of this new framework that should reorient the way in which self-assessment "skills" are conceptualized, taught, and evaluated in medical school and beyond.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".