A descriptive study to assess the knowledge regarding sudden infant death syndrome among the postnatal mothers with a view to develop an information booklet in selected hospitals of Jalandhar, Punjab, 2019
Bibliographic record
Abstract
Despite declines in prevalence during the past two decades, Sudden Infant Death Syndrome (SIDS) is one of the main causes of Child mortality globally. Sudden Infant Death Syndrome is responsible for at least one quarter of all Child deaths worldwide. Infant mortality and morbidity is leading issue now a days thus, the present study was conducted on postnatal mothers to assess the level of knowledge and educate them about prevention and reducing the occurrence of the complications.Aim: To enhances the knowledge of postnatal mothers regarding sudden infant death syndrome and provides them an information booklet.Method and Material: Non probability convenient sampling technique was used to select the 200 samples for the study. A Self Structure Knowledge questionnaire was used to collect data.Statistical Analysis: Collected data was analysed by using descriptive and inferential statistics.Result: A Descriptive study was conducted on 200 postnatal mothers of SGL Charitable Hospital, Civil Hospital, Armaan Hospital, Global Hospital, Amar Hospital, Holy Family Hospital, Alpex Hospital, and Johal Hospital in the month of February 2019 to assess the level of knowledge of postnatal mothers.Conclusion: The result showed that out of 200 postnatal mothers 99(49.5%) had average knowledge, 71(35.5%) had poor knowledge and 30(15%) had good knowledge regarding Sudden Infant Death Syndrome.The study concluded that Knowledge regarding Sudden Infant Death Syndrome may help in reducing the occurrence of the complications.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".