Bibliographic record
Abstract
Abstract This integrative review sought knowledge across a broad spectrum of literature concerning the role of color and light in maternity environments. Today it is acknowledged that the clinical nature of birth spaces is detrimental to maintaining normal physiological birth rates. Significantly, “clinical” spaces are often described as white, pale, monochromatic, and/or overlit. Attempts to make maternity settings more “home‐like” have promoted use of “warm” or “soft” colors. Ambience or spatial atmosphere is known to impact birth hormones, affecting labor commencement and progress. Today, efforts to improve birth spaces include “sensory rooms” (offering pain distraction via dark spaces and illuminated color elements); programmable colored light installations; and immersive image projections. Yet, as this paper shows, there is little specific study of the physical and psychological impact of color and light within birth settings. However, there are significant findings on colored light's impact upon birth processes, including the contraindication of bright blue light. And there is valuable knowledge embedded in old and new literature from diverse disciplines. This review thus exposes the strong need for further research and literature focused directly on how color and light in birth environment design impact birth experience for all involved. It is clear that environmental color and light need to be taken seriously as potent interrelated environmental factors that are directly implicated in the health and wellness of mothers and their infants during labor and birth. Thus, it is crucial to bring deeper awareness and comprehensive knowledge into use by designers, developers and managers of birth spaces.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".