Physicians' assessment of the value of clinical information: Operationalization of a theoretical model
Bibliographic record
Abstract
Abstract Inspired by the acquisition–cognition–application model (T. Saracevic & K.B. Kantor, 1997 ), we developed a tool called the Information Assessment Method to more clearly understand how physicians use clinical information. In primary healthcare, we conducted a naturalistic and longitudinal study of searches for clinical information. Forty‐one family physicians received a handheld computer with the Information Assessment Method linked to one commercial electronic knowledge resource. Over an average of 320 days, 83% of 2,131 searches for clinical information were rated using the Information Assessment Method. Searches to address a clinical question, as well as the retrieval of relevant clinical information, were positively associated with the use of that information for a specific patient. Searches done out of curiosity were negatively associated with the use of clinical information. We found significant associations between specific types of cognitive impact and information use for a specific patient. For example, when the physician reported “My practice was changed and improved” as a result of this clinical information, the odds that information was used for a specific patient increased threefold. Our findings provide empirical data to support the applicability of the acquisition‐cognition‐application model, as operationalized through the Information Assessment Method, in primary healthcare. Capturing the use of research‐based information in medicine opens the door to further study of the relationships between clinical information and health outcomes.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".