The Communication AssessmenT Checklist in Health (CATCH): A Tool for Assessing the Quality of Printed Educational Materials for Clinicians
Bibliographic record
Abstract
INTRODUCTION: There is little guidance available on strategies to improve the communication quality of printed educational materials (PEMs) for clinicians. The purposes of this study were to conceptualize PEM communication quality, develop a checklist based on this conceptualization, and validate the checklist with a selection of PEMs. METHODS: From a literature review of the strategies influencing communication quality, we generated a conceptual map and developed the Communication AssessmenT Checklist in Health (CATCH) consisting of 55 items nested in 12 concepts. Two raters independently applied CATCH to 45 PEMs evaluated in the studies included in a Cochrane systematic review. From these results, we conducted an item analysis and assessed content validity of CATCH using a hierarchical cluster analysis to explore the extent to which our CATCH operationalization truly represented the communication quality concepts. RESULTS: Some concepts were better covered in the studied PEMs, whereas others were not covered consistently. We observed 3 contrasting PEM clusters. A first cluster (n = 22) was characterized by longer PEMs and comprised mostly high-impact peer-reviewed scientific articles or clinical practice guidelines. A second cluster (n = 22) consisted of PEMs shorter than 4 pages that used special fonts, color, pictures, and graphics. A third cluster consisted of a single brief PEM. DISCUSSION: With CATCH it is possible to categorize and understand the mechanisms that can trigger a change in behavior in health care providers. Additional research is needed to validate CATCH before it can be recommended for use.
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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 | 0.081 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".