Public awareness of the EMS system in Western Saudi Arabia: identifying the weakest link
Bibliographic record
Abstract
BACKGROUND: The City of Jeddah is the major and largest city in the Western Region of the Kingdom of Saudi Arabia (KSA). Covering a total area of 748 km2. The Saudi Red Crescent Organization (SRC) makes up the major bulk of the Emergency Medical Service (EMS) system in the Kingdom. We have set out to investigate the level of public awareness of the EMS system in place in Western KSA. METHOD: This study was an observational cross-sectional study that was done by interviewing the general public in public venues. The survey consisted of a two part questionnaire. The first part was completed for all subjects. The second part was completed only for those subjects that had previous experience with the SRC service. RESULT: A total of 1534 subjects were interviewed by 5 data collectors. 33% of people did not know the emergency dispatcher number to call in case of a medical emergency. The majority estimated the ETA of an ambulance response to their home to be about 30 minutes or more. 94 % said that MEDEVAC is needed. 17.7 % of people still find it unacceptable for male paramedics to respond to a female emergency unescorted by a male family member. CONCLUSION: It is clear that the general public is aware of the deficit in EMS coverage that is present. To improve the public awareness of the EMS system, municipal, legislative, public guidance, as well as religious support, are needed to be utilized to improve the community's satisfaction and quality of care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".