How to Develop an Online Video for Teaching Health Procedural Skills: Tutorial for Health Educators New to Video Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Clinician educators are experts in procedural skills that students need to learn. Some clinician educators are interested in creating their own procedural videos but are typically not experts in video production, and there is limited information on this topic in the clinical education literature. Therefore, we present a tutorial for clinician educators to develop a procedural video. OBJECTIVE: We describe the steps needed to develop a medical procedural video from the perspective of a clinician educator new to creating videos, informed by best practices as evidenced by the literature. We also produce a checklist of elements that ensure a quality video. Finally, we identify the barriers and facilitators to making such a video. METHODS: We used the example of processing a piece of skeletal muscle in a pathology laboratory to make a video. We developed the video by dividing it into 3 phases: preproduction, production, and postproduction. After writing the learning outcomes, we created a storyboard and script, which were validated by subject matter and audiovisual experts. Photos and videos were captured on a digital camera mounted on a monopod. Video editing software was used to sequence the video clips and photos, insert text and audio narration, and generate closed captions. The finished video was uploaded to YouTube (Google) and then inserted into open-source authoring software to enable an interactive quiz. RESULTS: The final video was 4 minutes and 4 seconds long and took 70 hours to create. The final video included audio narration, closed captioning, bookmarks, and an interactive quiz. We identified that an effective video has six key factors: (1) clear learning outcomes, (2) being engaging, (3) being learner-centric, (4) incorporating principles of multimedia learning, (5) incorporating adult learning theories, and (6) being of high audiovisual quality. To ensure educational quality, we developed a checklist of elements that educators can use to develop a video. One of the barriers to creating procedural videos for a clinician educator who is new to making videos is the significant time commitment to build videography and editing skills. The facilitators for developing an online video include creating a community of practice and repeated skill-building rehearsals using simulations. CONCLUSIONS: We outlined the steps in procedural video production and developed a checklist of quality elements. These steps and the checklist can guide a clinician educator in creating a quality video while recognizing the time, technical, and cognitive requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it