Bibliographic record
Abstract
Problem being addressed Medical emergencies occur commonly in offices of family physicians, yet many offices are poorly prepared for emergencies. An Internet-based educational video discussing office emergencies might improve the responses of physicians and their staff to emergencies, yet such a tool has not been previously described. Objective of program To use evidence-based practices to develop an educational video detailing preparation for emergencies in medical offices, disseminate the video online, and evaluate the attitudes of physicians and their staff toward the video. Program description A 6-minute video was created using a review of recent literature and Canadian regulatory body policies. The video describes recommended emergency equipment, emergency response improvement, and office staff training. Physicians and their staff were invited to view the video online at [www.OfficeEmergencies.ca][1] . Viewers’ opinions of the video format and content were assessed by survey (n = 275). Conclusion Survey findings indicated the video was well presented and relevant, and the Web-based format was considered convenient and satisfactory. Participants would take other courses using this technology, and agreed this program would enhance patient care. [1]: http://www.OfficeEmergencies.ca
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.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.479 | 0.167 |
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".