Interrater Reliability of the Adapted Fresno Test across Multiple Raters
Bibliographic record
Abstract
PURPOSE: The Adapted Fresno Test (AFT) is a seven-item instrument for assessing knowledge and skills in the major domains of evidence-based practice (EBP), including formulating clinical questions and searching for and critically appraising research evidence. This study examined the interrater reliability of the AFT using several raters with different levels of professional experience. METHOD: The AFT was completed by physiotherapists and occupational therapists, and a random sample of 12 tests was scored by four raters with different levels of professional experience. Interrater reliability was calculated using intra-class correlation coefficients (ICC [2, 1]) for the individual AFT items and the total AFT score. RESULTS: Interrater reliability was moderate to excellent for items 1 and 7 (ICC=0.63-0.95). Questionable levels of reliability among raters were found for other items and for the total score. For these items, the raters were clustered into two groups-"experienced" and "inexperienced"-and then examined for reliability. The reliability estimates for rater 1 and rater 2 ("inexperienced") increased slightly for items 2 and 5 and for the total score, but not for other items. For raters 3 and 4 ("experienced"), ICCs increased considerably, indicating excellent reliability for all items and for the total score (0.80-0.99), except for item 4, which showed a further decrease in ICC. CONCLUSION: Use of the AFT to assess knowledge and skills in EBP may be problematic unless raters are carefully selected and trained.
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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 | 0.097 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".