International Federation of Emergency Medicine campaign on crowding
Bibliographic record
Abstract
Background: The international crisis of emergency department crowding, and access block has reached new heights with the fallout from the COVID-19 pandemic. Many ED’s around the globe continue to overflow their departments with “hallway medicine” becoming the new norm for many countries. The International Federation of Emergency Medicine has responded to this health equity issue with the creation of an international campaign against crowding. With this campaign, IFEM hopes to move research into action by creating an international campaign to highlight why overcrowding matters and what physicians, hospital administrators, and health ministries can do to combat this issue. Methods: International relations have been vital throughout the development and implementation of this campaign. To date the implementation plan is to launch a social media presence the first two weeks of December 2022 where we put patient stories and on the ground solutions at the forefront. This movement is a multi-tiered strategy to use social media and the public to bring awareness to this health equity issue. We have created position statements, social media content, IFEM press releases, and pre-written letters for physician use to politicians or hospital administrators. Throughout this process we have also been gathering international news articles that detail the lethal consequences of crowding that we plan to display during the campaign. Evaluation Methods: The campaign will be evaluated via social media measures such as retweets/likes on Twitter, likes on Instagram, and hashtag use. In addition, there will be a post-campaign survey that campaign members and stakeholders can fill out to discuss the successes and possible areas of improvement for future campaign efforts. Evaluation of the campaign on an ongoing basis will be important to ensure that the finite resources of the organization is used to create the biggest impact. Results: Results for campaign launch will become available the third week of December 2022. Preliminary results show an overwhelming emergency physician desire for an international movement so that clinicians can show the immense impacts this issue has caused within their departments. There has also been voting on the social media content within the project team with the hashtags #NoMoreLivesLostWaiting and #ResetEmergencyCare to be the most effective messaging. Advice and Lessons Learned: Ensure that you have a strong team of support and dedicated project leads – having strong clinicians willing to take on a specified area of the campaign played a vital role in the success of this campaign thus far Build off the successes and failures of others; a key role in the development of this campaign was seeing what initiatives have gone on in the past and how can ours utilize their accomplishments and learn from their failures Keeping a positive attitude is vital; when discussing an emotionally charged issue like crowding, keeping a positive outlook is important to uplift the spirits of those around you who may be suffering the ED harms of crowding such as moral injury and burnout Conflicts of Interest: none to disclose Author Statement: TM wrote the abstract and presented the work at the ESCN QI forum. EL has been vital for project guidance as he has been a key player in the development and implementation of this campaign.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".