The FFI was revised, and new items were added to compose the FFI-R. The chief theoretical change was adding a psychosocial scale. Both long and short forms had very good psychometric properties.

This study examined the reliability and construct validity of a modified version of the Colorado Symptom Index (MCSI), a brief, self-report measure of psychological symptomatology, in a study of interventions to prevent homelessness. Eight projects in a national, cooperative study collected new data at baseline, 6, and 12 months using a set of common measures as well as site-specific instruments. The pooled sample consisted of 1,381 persons in treatment for mental illness or substance abuse (or both), of which 84% had a history of homelessness. The analyses employed classical and Rasch methods to examine the MCSI's content validity, internal consistency and item quality, test/retest reliability, dimensionality, appropriateness for the sample, construct validity, and responsiveness to change. This 14-item scale was found to be a reliable and valid measure of psychological symptoms in this sample. Its content was consistent with other symptom measures, its high internal consistency and test-retest coefficients supported its reliability, its relationships to other measures indicated that it had good construct validity, and it was responsive to change. We conclude that the MC

Use of a coordinated representative payee program was found to be effective in improving outcomes at 12 months. Although this evidence supports the wider implementation of a coordinated representative payee program, only 31 percent of the experimental group had their money banked with a representative payee. Therefore, future studies should focus on achieving a better understanding of the causal components of the intervention.

Findings from this pre- and postintervention retrospective study are tentative in the absence of a more rigorous design. However, the results suggest that the representative payee program is quite effective in reducing hospital stays.

BackgroundThe Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safety. Reports about adverse events following immunization (AEFI) from surveillance systems contain free-text components that can be analyzed using natural language processing. To extract Unified Medical Language System (UMLS) concepts from free text and classify AEFI reports based on concepts they contain, we first needed to clean the text by expanding abbreviations and shortcuts and correcting spelling errors. Our objective in this paper was to create a UMLS-based spelling error correction tool as a first step in the natural language processing (NLP) pipeline for AEFI reports.MethodsWe developed spell checking algorithms using open source tools. We used de-identified AEFI surveillance reports to create free-text data sets for analysis. After expansion of abbreviated clinical terms and shortcuts, we performed spelling correction in four steps: (1) error detection, (2) word list generation, (3) word list disambiguation and (4) error correction. We then measured the performance of the resulting spell checker by comparing it to manual correction.ResultsWe used 12,056 words to train the spell checker and tested its performance on 8,131 words. During testing, sensitivity, specificity, and positive predictive value (PPV) for the spell checker were 74% (95% CI: 74–75), 100% (95% CI: 100–100), and 47% (95% CI: 46%–48%), respectively.ConclusionWe created a prototype spell checker that can be used to process AEFI reports. We used the UMLS Specialist Lexicon as the primary source of dictionary terms and the WordNet lexicon as a secondary source. We used the UMLS as a domain-specific source of dictionary terms to compare potentially misspelled words in the corpus. The prototype sensitivity was comparable to currently available tools, but the specificity was much superior. The slow processing speed may be improved by trimming it down to the most useful component algorithms. Other investigators may find the methods we developed useful for cleaning text using lexicons specific to their area of interest.