The Clinical Relevance of Information Index (<scp>CRII</scp>): assessing the relevance of health information to the clinical practice
Bibliographic record
Abstract
BACKGROUND: The high volume of health information creates a need for processes and tools to select, evaluate and disseminate relevant information to health professionals in clinical practice. OBJECTIVES: To introduce an index of the clinical relevance of information and to show that it is different from existing measures. METHODS: A conceptual model of knowledge translation was developed to explain the need for a new index, whose application was verified by an exploratory study with two (quantitative and qualitative) phases. The Clinical Relevance of Information Index (CRII) was defined employing descriptive statistical analyses of assessments performed by health professionals. The model and the CRII were applied in a primary healthcare context. RESULTS: The CRII was applied to 4574 relevance assessments of 194 evidence synopses. The assessments were performed by 41 family physicians in 2008. The CRII value of each synopsis was compared with the number of citations received by its corresponding research paper and with the level of evidence of the study, presenting weak correlation with both. CONCLUSION: The CRII captures aspects of information not considered by other indices. It can be a parameter for information providers, institutions, editors, as well as health and information professionals targeting knowledge translation.
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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 | 0.090 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.036 | 0.032 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".