Bed Sharing, <scp>SIDS</scp> Research, and the Concept of Confounding: A Review for Public Health Nurses
Bibliographic record
Abstract
Confounding is an important concept for public health nurses (PHNs) to understand when considering the results of epidemiological research. The term confounding is derived from Latin, confundere, which means to "mix-up" or "mix together". Epidemiologists attempt to derive a cause and effect relationship between two variables traditionally known as the exposure and disease (e.g., smoking and lung cancer). Confounding occurs when a third factor, known as a confounder, leads to an over- or underestimate of the magnitude of the association between the exposure and disease. An understanding of confounding will facilitate critical appraisal of epidemiological research findings. This knowledge will enable PHNs to strengthen their evidence-based practice and better prepare them for policy development and implementation. In recent years, researchers and clinicians have examined the relationship between bed sharing and sudden infant death syndrome (SIDS). The discussion regarding the risk of bed sharing and SIDS provides ample opportunity to discuss the various aspects of confounding. The purpose of this article is to use the bed sharing and SIDS literature to assist PHNs to understand confounding and to apply this knowledge when appraising epidemiological research. In addition, strategies that are used to control confounding are discussed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".