Enhancing the Awareness of Osteopathic Learning Opportunities Through a Newsletter
Bibliographic record
Abstract
Abstract Background: Medical professionals have historically received timely information through newsletters that deliver the most up-to-date information. Such newsletters save the reader time in searching through resources by providing them information in succinct format. Therefore, a central Ohio institution with many osteopathic post-graduate programs created a monthly osteopathic newsletter to communicate its learning opportunities in an effective manner. Objective: To identify the frequency of medical staff utilizing a monthly osteopathic newsletter, the items valued most in a newsletter, and the preferred method of obtaining medical news. Methods: The osteopathic newsletter was put into circulation among medical education staff, residents, fellows, and affiliated medical students for one year between December 2021 and November 2022. After a year, a survey was sent to all newsletter recipients through the Research Electronic Data Capture (REDCap) system program and respondents answered questions regarding readership, engagement, and suggested areas of improvement. The data was compiled over the course of six weeks, analyzed using qualitative statistics, and used to implement changes in newsletter layout, content, and distribution. The newsletter was advertised as “new and improved” with this reflective content. After an additional six months of distribution, a follow-up survey was sent in June 2023. Comparisons were made between the first and second surveys using chi-squared tests and the p-value was set at 0.05. Results: There was statistically significant increase in readership from the first survey to the second survey at +27.5% (p<.0.001). Regarding content, the “News” and “Clinical Pearls” sections were top rated in both surveys and there were statistically significant increases in choice of these sections as time progressed (+15.5%, p = 0.024 for “News”; and +25.5%, p <0.001 for “Clinical Pearls,” respectively). While regular readership of the newsletter continued to grow throughout the survey, 95% of all respondents answered they had learned more about osteopathic medicine. Conclusions: This study suggests that e-newsletters like this osteopathic newsletter could be a successful option for systems to engage with groups, improve communication, and update readers on news and osteopathic educational content.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.014 | 0.002 |
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".