Using Clinical Questions Asked by Primary Care Providers Through eConsults to Inform Continuing Professional Development
Bibliographic record
Abstract
INTRODUCTION: Continuing professional development (CPD) offerings should address the educational needs of health care providers. Innovative programs, such as electronic consultations (eConsults), provide unique educational opportunities for practice-based needs assessment. The purpose of this study is to assess whether CPD offerings match the needs of physicians by coding and comparing session content to clinical questions asked through eConsults. METHODS: This study analyzes questions asked by primary care providers between July 2011 and January 2015 using a service that allows specialists to provide consultation over a secure web-based server. The content of these questions was compared with the CPD courses offered in the area in which these primary care providers are practicing over a similar period (2012-2014). The clinical questions were categorized by the content area. The percentage of questions asked about each content area was calculated for each of the 12 specialties consulted. CPD course offerings were categorized using the same list of content areas. Percentage of minutes dedicated to each content area was calculated for each specialty. The percentage of questions asked and the percentage of CPD course minutes for each content area were compared. RESULTS: There were numerous congruencies and discrepancies between the proportion of questions asked about a given content area and the CPD minutes dedicated to it. DISCUSSION: Traditional needs assessment may underestimate the need to address topics that are frequently the subject of eConsults. Planners should recognize eConsult questions as a valuable source of practice-associated challenges that can identify professional development needs of physicians.
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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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".