Development of a learning object for caring for the sensory environment in a neonatal unit: noise, light and handling
Bibliographic record
Abstract
Background : The environment in Neonatal Intensive Care Unit (NICU) can contribute to the occurrence of stressing situations involving the hospitalized newborn, the family and the health team, due to inappropriate sensory stimuli. Aim : To describe the experience of constructing the Learning Object (LO) about the sensory environment (noise, light and handling) in a NICU. Methods : A LO about the environment in a NICU was developed in Flash® for WEB, utilizing participative methodology in company with the unit’s team, focusing on sensitizing the team about the effects of noise, light and handling, and about the strategies for their reduction. Results : The participants understood the importance which attention paid to the environment of the NICU plays in the development of the newborn. The LO was organized into theoretical modules with the insertion of multimedia and a simulation module. Conclusion : The LO is considered appropriate for use in the training of nurses and in the educating of NICU health care professionals on the neonatal unit’s environment. For this teaching tool’s efficacy in the education of health teams and the family to be verified, however, it will need to be applied practically.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".