Evaluating the readability of recruitment materials in veterinary clinical research
Bibliographic record
Abstract
BACKGROUND: Owner comprehension is vital to recruitment and study success, but limited information exists regarding the readability of public-facing veterinary clinical trial descriptions. OBJECTIVES: The current study sought to evaluate the readability of public-facing online veterinary clinical trial descriptions from academic institutions and private referral practices. ANIMALS: None. METHODS: This prospective study assessed readability in a convenience sample of veterinary clinical trial study descriptions using 3 common methods: the Flesch-Kincaid Grade Level (F-K), Flesch Reading Ease Score (FRES), and online Automatic Readability Checker (ARC). Results were compared across specialties and between academic and private institutions. RESULTS: Grade level and readability consensus scores (RCSs) were obtained for 61 online clinical trial descriptions at universities (n = 49) and private practices (n = 12). Average grade-level RCS for study descriptions was 14.13 (range, 9-21). Using Microsoft Word, the FRES score was higher in descriptions from universities compared to private practices (P = .03), and F-K scores were lower in university compared to private practice descriptions (P = .03). FRES (P = .07), F-K (P = .12), and readability consensus (P = .17) scores obtained from ARC were not different between institution types. Forty-eight studies (79%) had RCSs over 12, equivalent to reading material at college or graduate school levels. CONCLUSIONS AND CLINICAL IMPORTANCE: Similar to other areas in veterinary communication, the evaluated veterinary clinical trial descriptions used for advertising and recruitment far exceeded the recommended 6th-grade reading level for medical information. Readability assessments are straightforward to conduct, and ensuring health literacy should be a customary best practice in veterinary medicine and clinical research.
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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 | 0.075 | 0.245 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".