Bibliographic record
Abstract
Objectives/goals: This project will focus on identifying the barriers that result in low adherence to quality care indicators that establish effective and efficient pediatric emergency care. The overall objective is to understand motivations behind adherence (or lack thereof) and find solutions to facilitate compliance. Methods/Study Population: This study will use a mixed-methods design to investigate the barriers. Quantitative data will be collected through a survey provided to healthcare providers involved in pediatric emergency care, including physicians, nurses, and administrative staff in both pediatric and general hospitals. Qualitative data will be collected through semi-structured interviews with a group of respondents to gain insight on their experience regarding compliance. Quantitative data will be analyzed using statistical analyses while qualitative data will undergo a thorough thematic analysis. Both sets of data will be reviewed to identify themes and differences in barriers across hospital types and healthcare roles. Results/Anticipated Results: We will have gathered insights and perspectives from key stakeholders that are relevant to our study to ensure a comprehensive understanding of any potential implications that may arise from our study. We anticipate that the specific results will highlight key differences in adherence between pediatric and general hospitals. The study is expected to identify specific barriers hindering compliance with established guidelines in both settings. The results may be used to increase adherence to critical quality indicators and improve patient care. Discussion/Significance of Impact: Pediatric injury care prioritizes the immediacy of care for children with acute illness and injury. With certain hospital protocols not being adhered to, there is a risk of wasting crucial time and resources that can affect patient care outcomes. The results would provide recommendations to improve and increase efficiency in pediatric injury care.
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 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 | 0.030 | 0.010 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".