The Educational Utility of Blogging for MRI Technologists
Bibliographic record
Abstract
INTRODUCTION: The value of a blog as an educational tool is thought to be underestimated by health care professionals. This research aimed to explore the MRI educational utility of blogs, and to determine who was participating in writing those blogs. It was hoped that this research would increase awareness of alternative education formats that would be useful for MRI technologists. METHODS: Between March and April of 2017, an online blog search was performed using MRI-related keywords. Strict exclusion criteria were then applied. Two coders independently used lean coding to analyse selected blog posts and organized the codes into themes. Data were tested for intercoder reliability. RESULTS: Researchers analysed 39 posts from 9 blogs and identified the following themes: focus on MRI techniques and technologies, knowledge dissemination, sharing of experience, collaborative learning, authorship, and informal writing. Bloggers, self-identified as practitioners or scholars, communicated about research projects and used an informal writing style. Evidence of intentional teaching of MRI-specific content and sharing of professional and personal experiences was found. Communication between authors and readers from most of the MRI professions was observed, with the exception of MRI technologists. CONCLUSIONS: This research found that MRI-related blogs provide a credible and accessible forum for the sharing and discussion of knowledge, experiences, and ideas. Although many MRI professionals author blogs, MRI technologists do not seem to participate in this form of communication. As social media gains in popularity within the medical radiation technologist profession, it is hoped that more MRI technologists will make use of blogging to facilitate learning, collaboration, and communication.
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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 | 0.007 | 0.055 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".