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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".