Prevalence and Factors Associated with Hypothermia among Neonates in Regional Referral Hospitals in Dar es Salaam, Tanzania
Bibliographic record
Abstract
BACKGROUND: Neonatal hypothermia is a major cause of mortality. This study determined the prevalence and factors associated with neonatal hypothermia in two regional referral hospitals in Dar es Salaam, Tanzania. METHODS: Cross-sectional study was carried out between March and May 2021 at the Mwananyamala and Temeke Regional Referral Hospitals. Simple random and stratified sampling procedures were used to select study sites and proportionate population samples from each hospital respectively. Body temperature was measured within 90 minutes post birth; knowledge of the WHO guidelines on thermal protection of new-borns was collected from the mothers and health care providers using questionnaires. Logistic regression was used to assess associations between variables. SPSS version 25 was used to analyse the data and p < 0.05 was considered significant. RESULTS: Total of 296 mother-new-born pairs and 41 health care providers were enrolled in the study. 26 mothers did not consent for the study. 25.6% of the 270 studied neonates were hypothermic. Lack of skin-to-skin contact with the mother; early neonatal weighing and bathing increased likelihood of neonatal hypothermia. Knowledge of neonatal thermal protection among mothers and care-providers was inadequate. CONCLUSIONS: The prevalence of neonatal hypothermia among neonates in the referral hospitals is high. The findings suggest knowledge gaps of the WHO recommended guidelines on neonatal hypothermia are associated with neonatal hypothermia. Efforts to increase awareness of the WHO recommended thermal protection guidelines are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".