Health Care Providers’ Profiles and Evaluations of a Statewide Online Education Program for Dissemination of Clinical Evidence on HIV, Hepatitis C Virus, and Sexually Transmitted Disease: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Timely and effective dissemination of the latest clinical evidence to health care providers is essential for translating biomedical research into routine patient care. Online platforms offer unique opportunities for dissemination of medical knowledge. OBJECTIVE: In this study, we report the profiles of health care providers participating in the New York State HIV-HCV-STD Clinical Education Initiative online program and their evaluations of the online continuing professional development courses. METHODS: We compiled professional and personal background information of the clinicians who completed at least one online course. We collected their self-reported program evaluation data with regard to the course content, format, knowledge increase, and impact on clinical practice. RESULTS: We recorded a total of 4363 completions of 88 online courses by 1976 unique clinicians during a 12-month study period. The clinicians' background was diverse in terms of demographics, education levels, professional disciplines, practice years, employment settings, caseloads, and clinical services. The evaluation of online courses was very positive (usefulness/relevance, 91.08%; easy comprehension, 89.09%; knowledgeable trainer, 92.00%; appropriate format, 84.35%; knowledge increase, 48.52%; intention to use knowledge, 85.26%; and plan to change practice, 21.98%). Comparison with the reference data indicated that the online program successfully reached out to the primary care communities. Both the younger generation and the senior health care providers were attracted to the online program. High-quality multimedia resources, flexibility of access, ease of use, and provision of continuing professional development credits contributed to the initial success of this online clinical education program. CONCLUSIONS: We have successfully characterized a diverse group of clinicians participating in a statewide online continuing professional development program. The evaluation has shown effective use of online resources to disseminate clinical evidence on HIV, hepatitis C virus, and sexually transmitted disease to primary care clinicians.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".