Construct validity and reliability of the Concussion Knowledge Assessment Tool (CKAT).
Bibliographic record
Abstract
OBJECTIVE: To evaluate the test-retest reliability and construct validity of the concussion knowledge assessment tool (CKAT) as a measure of knowledge of concussion and its management among chiropractic subgroups and to compare these properties for two scoring strategies for the CKAT. METHODS: Three chiropractic subgroups (first year students, interns and sports chiropractors) completed the CKAT via SurveyMonkey with as second administration two to six weeks later for a subset of respondents. Scatter plots and Intraclass Correlation Coefficients (ICC) were used for test-retest reliability. A priori hypotheses regarding the relationship of CKAT scores across known subgroups, and with concussion knowledge self-rankings were established prior to data collection. Distributions of CKAT scores were compared across the subgroups using boxplots and ANOVA for known groups validity, and correlation of CKAT scores with concussion knowledge self-ranking was examined. RESULTS: =17.54; p<0.0001). CONCLUSIONS: The CKAT distinguished between chiropractic subgroups expected to have different levels of knowledge, supporting construct validity, however, it did not achieve adequate test-retest reliability.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Psychometric validation of a concussion knowledge instrument; domain measurement validation rather than a study of research practice.
It evaluates the validity and reliability of a concussion knowledge instrument, not research methods themselves.
Psychometric validation of a clinical concussion-knowledge tool for chiropractors, not research methods.
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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".