Point‐of‐care Resource Use in the Emergency Department: A Developmental Model
Bibliographic record
Abstract
BACKGROUND: Technologic advances, free open-access medical education (FOAM or #FOAMed), and social media have increased access to clinician-oriented medical education resources and interactions at the point of care (POC); yet, how, when, and why medical providers use these resources remains unclear. To facilitate the development and design of intuitive POC resources, it is imperative that we expand our understanding of physician knowledge-seeking behavior at the POC. METHODS: Individual semistructured interviews were conducted and analyzed using a qualitative, grounded theory approach. Twelve emergency medicine providers (three medical students, three residents, and six attending physicians) were interviewed in person or via video chat to explore how POC resources are used in the emergency department (ED). A coding system was developed by two investigators and merged by consensus. A third investigator audited the analysis. RESULTS: A conceptual framework emerged from the data describing the four main uses of POC resources (deep-dive, advanced clinical decision making, teaching patients, and teaching learners) and how practitioners' main use varied based on medical expertise. Junior learners prioritize their own broad learning. Experienced learners and physicians prefer to 1) seek answers to specific focused clinical questions and 2) disseminate POC information to teach patients and learners, allowing them to devote more of their time to other clinical and teaching tasks. CONCLUSION: The conceptual framework describes how physician knowledge-seeking behavior using POC resources in the ED evolves predictably throughout training and practice. Knowledge of this evolution can be used to enhance POC resource design and guide bedside teaching strategies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".