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To identify the patient classification systems used to classify nursing intensity in the assessment of nursing staffing resources currently used in home health care, with a special emphasis on validity, reliability and staff allocation. Scoping review of internationally published and grey literature, based on a methodological framework by Arksey and O'Malley. Searches of the electronic databases Cinahl, Medline, Embase and SweMed, the websites Google and Google Scholar and hand searches of reference lists occurred. Eligibility criteria included (A) a focus on patient classification systems measuring nursing intensity and workload in home health care and (B) published in English between January 2007 and March 2019. In level one testing two team members screened titles and abstracts, in level two testing two team members determined which papers should undergo a full text review. Data were extracted using structured extraction by one team member and verified by two other members. Thirteen peer-reviewed articles and grey literature documents were identified, from Canada, Ireland, the UK, the USA, Scotland, Turkey and the Netherlands. Four patient classification systems had been tested for both validity and reliability. Validity was tested through face validity, predictive validity, concurrent validity or content validity index. Reliability was tested through stability, internal consistency, observer agreement or inter rater reliability. One patient classification system had been tested only for reliability, through interrater reliability and observer agreement. Two patient classification systems had been evaluated through summative evaluation; one qualitatively through focus group interviews and one through semi-structured interviews. Only one patient classification system had been validity and reliability tested and evaluated. Overall, the patient classification systems in the included papers (13) were considered to have benefits and to be appropriate for the measurement of patients' needs, workload and allocation of staff, although specific information was not always given. Little has been published on validity or reliability tested patient classification systems linked to staffing allocation in home health care in the past decade. Limited research was seen where a patient classification system was considered to be fully operational in home health care. [ABSTRACT FROM AUTHOR]

Abstract: Background: We have proposed that declines in adaptive capacity, defined as the ability to adapt to multiple stressors, may serve as an indicator of risk for fatigue. A comprehensive measure of adaptive capacity does not exist. Objectives: In this paper we describe construction of an instrument to measure adaptive capacity, the Adaptive Capacity Index (ACI). Design: Descriptive and psychometric. Setting: Six sites providing palliative care in Western Canada. Participants: Inclusion criteria: ≥18 years old, diagnosed with advanced cancer, able to read and write English, Mini-Mental Status Exam score ≥22. Pilot study n =48; Main study n =225 stratified using the Edmonton Symptom Assessment Scale (ESAS) tiredness score (≥0 to ≤2 n =60; ≥3 to ≤6 n =108; ≥7 and ≤10 n =57). Methods: Following ethics approval, 17 experts in symptom management assisted with content validation and consenting individuals completed the Functional Assessment of Cancer Therapy-Fatigue (FACT-F), the Profile of Mood States-Vigor short form (POMS-Vsf), and the ACI. A research assistant collected demographic information and assigned an Eastern Cooperative Oncology Group (ECOG) score. Data were analyzed using descriptive and inferential statistics (i.e., exploratory factor analyses, correlation, multivariate analyses of variance, and multiple regression). Results: Five 6-item ACI factors/subscales (Cognitive Function, Stamina/Muscle Endurance, Sleep Quality, Emotional Reactivity, and Social Interaction) were identified. The ACI-total scale and its subscales were internally consistent (Cronbach''s alpha 0.76–0.89), and were significantly correlated with each other, and with each fatigue measure (Pearson''s r ranging from −0.724 to 0.634). The ACI total score was sensitive to changes in the ESAS tiredness score. Stamina/Muscle Endurance, Cognitive Function, and Sleep Quality predicted 60.8% of the variance in FACT-F. Stamina/Muscle Endurance and Social Interaction predicted 36.8% of the variance in POMS-Vsf. Stamina/Muscle Endurance and Sleep Quality predicted 8% of the variance in ECOG. Conclusions: The ACI is reliable and has beginning evidence of validity. In future studies we will examine relationships between ACI subscale scores and subsequent increases in fatigue and explore linkages to physiological processes. We will also establish ACI norms for early and late stage cancers and explore variations in ACI subscale scores base on age or gender. [Copyright &y& Elsevier]