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0-2: A score of 0-2 indicates no change in a child's status and regular rounding is acceptable. The plan of care will be continued as is. [35] 3-4: Indicates that a child's care is worsening, but they do not need immediate assistance. The plan of care may change or continued close monitoring will be initiated. [35]
Nursing assessment is the gathering of information about a patient's physiological, psychological, sociological, and spiritual status by a licensed Registered Nurse. Nursing assessment is the first step in the nursing process. A section of the nursing assessment may be delegated to certified nurses aides.
The Denver Developmental Screening Test (DDST) was introduced in 1967 to identify young children, up to age six, with developmental problems. A revised version, Denver II, was released in 1992 to provide needed improvements. These screening tests provide information about a range of ages during which normally developing children acquire certain ...
Family-centered practices (FCPs) use a variety of different tools for child development, [1] where the development, provision, and assessment of healthcare is equally constructive to both children and their families. FCP is valuable to clients of all children and can be applied in many different healthcare settings.
The FLACC scale or Face, Legs, Activity, Cry, Consolability scale is a measurement used to assess pain for children between the ages of 2 months and 7 years or individuals that are unable to communicate their pain. The scale is scored in a range of 0–10 with 0 representing no pain.
The Kaufman Assessment Battery for Children (KABC) is a clinical instrument (psychological diagnostic test) for assessing cognitive development. Its construction incorporates several recent developments in both psychological theory and statistical methodology.
The Ohio Youth Problems, Functioning, Satisfaction Scales (Ohio Scales) are a set of parent-reported [1] [2] and self-reported [3] questionnaires, consisting of four scales used to assess the improvement and outcomes of children and adolescents who have received mental health services.
[3] [4] Achenbach used machine learning and principal component analysis when developing the ASEBA in order to cluster symptoms together when forming the assessment's eight categories. This approach ignored the syndrome clusters found in the DSM-I, instead relying on patterns found in case records of children with identified psychopathologies.