A time and motion study in pediatric ER of the Secondary Hospital in Al Jouf Region, Saudi Arabia.
Bibliographic record
Abstract
OBJECTIVE: In the hospital setting, long waiting times and the lengthy formal process have increased the inefficiency and mismanagement resulting in the missing chance of saving the patients' life. Our aim was to assess the time wastage of every patient coming from reception to the actual emergency unit, to analyze the factor associated with the time lapse that occurs during every visit, and to see the effect of training on the services provided in the Pediatric emergency room. SUBJECTS AND METHODS: An intervention study was carried out in the following secondary care hospital in the Al Jouf region, Saudi Arabia: Esawiyah Hospital, Haditha Hospital, King Faisal Hospital, and Gurayat General Hospital among 400 study participants for 12 months. The study was carried out in 2 phases: pre-training, a period of training for hospital staff, and post-training data collection. Templates were generated on an MS Excel sheet and analysis of data was done using SPSS software. Percentages and proportions were calculated for descriptive statistics. RESULTS: Male and female patients were in the ratio of nearly 1:1. Training has significantly reduced the time to doctor consultation (U = 188, p < 0.001), and the time difference pre- and post-training from triage to consultation in a pediatric emergency is not significant (U = 16,769, p = 0.01). There is a strongly significant association (p < 0.001) between Canadian Triage and Acuity Scale (CTAS) implementation in triage. The practice of giving intravenous (IV) antibiotics in the emergency room has reduced significantly (p < 0.001) post-training. CONCLUSIONS: Training has a significant impact on the services provided in the pediatric emergency room.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".