What Do Primary Care Practitioners Want to Know? A Content Analysis of Questions Asked at the Point of Care
Bibliographic record
Abstract
INTRODUCTION: Assessing physician needs to develop continuing medical education (CME) activities is an integral part of CME curriculum development. The purpose of the present study was to demonstrate the feasibility of identifying areas of perceived greatest needs for continuing medical education (CME) by using questions collected electronically at the point of care. METHODS: This study is a secondary analysis of the "Just-in-Time" (JIT) information librarian consultation service database of questions using quantitative content analysis methods. The original JIT project demonstrated the feasibility of a real-time librarian service for answering questions asked by primary care clinicians at the point of care using a Web-based platform or handheld device. Data were collected from 88 primary care practitioners in Ontario, Canada, from October 2005 to April 2006. Questions were answered in less than 15 minutes, enabling clinicians to use the answer during patient encounters. RESULTS: Description of type and frequency of questions asked, including the organ system on which the questions focused, was produced using 2 classification systems, the "taxonomy of generic clinical questions" (TGCQ), and the International Classification for Primary Care version 2 (ICPC-2). Of the original 1889 questions, 1871 (99.0%) were suitable for analysis. A total of 970 (52%) of questions related to therapy; of these, 671 (69.2%) addressed questions about drug therapy, representing 36% of all questions. Questions related to diagnosis (24.8%) and epidemiology (13.5%) were also common. Organ systems questions concerning musculoskeletal, endocrine, skin, cardiac, and digestive systems were asked more than other categories. DISCUSSION: Questions collected at the point of care provide a valuable and unique source of information on the true learning needs of practicing clinicians. The TGCQ classification allowed us to show that a majority of questions had to do with treatment, particularly drug treatment, whereas the use of the ICPC-2 classification illustrated the great variety of questions asked about the diverse conditions encountered in primary care. It is feasible to use electronically collected questions asked by primary care clinicians in clinical practice to categorize self-identified knowledge and practice needs. This could be used to inform the development of future learning activities.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".