Pivoting injury prevention efforts during a pandemic: results of an international survey
Bibliographic record
Abstract
BACKGROUND: The COVID-19 a pandemic changed the world. Public health directives to socially distance with stay-at-home orders altered injury risk factor exposure, resulting injury patterns and conducting injury prevention (IP). The objective of this study was to determine the impact the COVID-19 pandemic on injury and IP at North American trauma centers (TC). RESULTS: Sixty-two responses were received from pediatric (44%), adult (11%), and combined (31%) TC, from 22 American states, 5 Canadian provinces and Australia. The majority (91%) of programs targeted age groups from birth to 15 years old. Nearly one-third reported IP to be less of an institutional priority with funding redistributed in 15% of centers [median (IQR) - 25% (- 43, 1)], and resultant staffing changes at 38% of centers. A decrease in IP efforts was reported at 64% of TC. Overall, the majority of respondents reviewed injury data, with the top reported increased mechanisms mainly intentional: Firearm-related (75%), assaults (72%), and abuse (71%). Leading increased unintentional injuries were injuries occurring in the home such as falls (70%), followed by ATV (62%), and cycling (57%). Sites pivoted by presenting (74%) or participating (73%) in IP education virtually, social media posts (61%) and the addition of technology (29%). Top barriers were redeployment of partners (45%) and staff (31%), as well as lack of technology (40%) in the target population. Facilitators were technology at TC (74%), support of trauma program (63%), and having IP funding maintained (55%). CONCLUSIONS: Nearly two-thirds of TC decreased IP efforts during the pandemic due to staffing and funding reductions. The leading reported increased injuries were intentional, indicating that violence prevention is needed, along with support for mental health. While TC successfully pivoted by using technology, access issues in the target population was a barrier resulting in health inequities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".