Leveraging machine learning to enhance nurses' practice for autism spectrum disorder care: A narrative review
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental condition marked by social interaction and communication difficulties, repetitive behaviors, and restricted interests. The increasing prevalence of ASD underscores the urgent need for specialized training for nurses who play a critical role in managing and supporting autistic individuals. Traditional practice methods often fail to equip nurses with the hands-on experience to interact with this population effectively. This literature review explores the transformative potential of machine learning (ML) in enhancing nurse training for ASD care. ML offers personalized and adaptive learning experiences, realistic simulations, continuous assessment, and evidence-based practices. Personalized learning through ML tailors educational content to individual nurses' needs, enhancing their competence and confidence. Realistic virtual simulations provide safe environments to practice real-life scenarios, improving nurses' ability to handle diverse behavioral and communication challenges. Continuous feedback and assessment ensure ongoing professional development, while data-driven insights support the creation of effective care strategies. Interdisciplinary collaboration is also facilitated through ML, integrating knowledge from various healthcare fields to offer comprehensive training. The improved training results in better patient outcomes, including fewer behavioral issues and enhanced interactions with autistic children. However, challenges such as ensuring data quality, ethical considerations, and fostering acceptance of new technologies must be addressed. This review highlights ML's significant impact on nurse training, aiming to elevate the quality of care for individuals with ASD through innovative, data-driven approaches.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".