P.123 Assessing level of awareness, attitudes and believes toward pediatric congenital neurosurgical conditions
Bibliographic record
Abstract
Background: Community awareness, attitudes and beleifs toward pediatric patients diagnosed with congenital neurosurgical conditions is not known in the Saudi population and the number of studies is few worldwide. Such attitudes have a direct impact on the quality of life of patients with these congenital conditions. This study aims to demonstrate the variation in awareness, attitudes and believes in the public and among health-care professionals towards patients diagnosed with congenital neurosurgical conditions and its associated factors. Methods: A survey consisting of 36 questions pertaining to Hydrocephalus, Brain Tumors and Spina Bifida awareness, attitudes and believes was distributed to Saudi citizens living in the eastern province older than 15 years of age among hospitals visitors, medical students, nutritionists, physicians, dentists, pharmacists, and nurses. Results: The analysis of the 1002 respondent of the questionnaire shows clear social stigmata and improper awareness, attitudes and believes toward pediatric patients diagnosed with congenital neurosurgical conditions. There are variable parameters on interest measured and analyzed and there are certain patterns observed as well. Conclusions: The analysis showed the importance of health education for the public to increase the level of awareness and it justifies why these factors should be addressed in the middle of patients’ management, community awareness and health planning.
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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".