Validity evidence as a key marker of quality of technical skill assessment in OTL–HNS
Bibliographic record
Abstract
OBJECTIVE: Quality monitoring of assessment practices should be a priority in all residency programs. Validity evidence is one of the main hallmarks of assessment quality and should be collected to support the interpretation and use of assessment data. Our objective was to identify, synthesize, and present the validity evidence reported supporting different technical skill assessment tools in otolaryngology-head and neck surgery (OTL-HNS). METHODS: We performed a secondary analysis of data generated through a systematic review of all published tools for assessing technical skills in OTL-HNS (n = 16). For each tool, we coded validity evidence according to the five types of evidence described by the American Educational Research Association's interpretation of Messick's validity framework. Descriptive statistical analyses were conducted. RESULTS: All 16 tools included in our analysis were supported by internal structure and relationship to variables validity evidence. Eleven articles presented evidence supporting content. Response process was discussed only in one article, and no study reported on evidence exploring consequences. CONCLUSION: We present the validity evidence reported for 16 rater-based tools that could be used for work-based assessment of OTL-HNS residents in the operating room. The articles included in our review were consistently deficient in evidence for response process and consequences. Rater-based assessment tools that support high-stakes decisions that impact the learner and programs should include several sources of validity evidence. Thus, use of any assessment should be done with careful consideration of the context-specific validity evidence supporting score interpretation, and we encourage deliberate continual assessment quality-monitoring. LEVEL OF EVIDENCE: NA. Laryngoscope, 128:2296-2300, 2018.
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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 | 0.362 | 0.749 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.030 | 0.022 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".