Systematically Assessing the Situational Relevance of Electronic Knowledge Resources: A Mixed Methods Study
Bibliographic record
Abstract
UNLABELLED: Electronic Knowledge Resources (EKRs) are increasingly used by physicians, but their situational relevance has not been systematically examined. OBJECTIVE: Systematically scrutinize the situational relevance of EKR-derived information items in and outside clinical settings. BACKGROUND: Physicians use EKRs to accomplish four cognitive objectives (C1-4), and three organizational objectives (O1-3): (C1) Answer questions/solve problems/support decision-making in a clinical context; (C2) fulfill educational-research objectives; (C3) search for personal interest or curiosity; (C4) overcome limits of human memory; (O1) share information with patients, families, or caregivers; (O2) exchange information with other health professionals; (O3) plan-manage-monitor tasks with other health professionals. METHODS: Longitudinal mixed methods multiple case study: Cases were 17 residents' critical searches for information, using a commercial EKR, during a 2-month block of family practice. Usage data were automatically recorded. Each "opened" item of information was linked to an impact assessment questionnaire, and 1,981 evaluations of items were documented. Interviews with residents were guided by log files, which tracked use and impact of EKR-derived information items. Thematic analysis identified 156 critical searches linked to 877 information items. For each case, qualitative data were assigned to one of the seven proposed objectives. RESULTS: Residents achieved their search objectives in 85.9% of cases (situational relevance). Additional sources of information were sought in 52.6% of cases. Results support the seven proposed objectives, levels of comparative relevance (less, equally, more), and levels of stimulation of learning and knowledge (individual, organizational). CONCLUSION: Our method of systematic assessment may contribute to user-based evaluation of EKRs.
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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 | 0.074 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".