Edi screening tool

Detailed information regarding the symptom areas assessed by the EDI-3 SC is necessary for determining whether patients meet the formal diagnostic criteria for an eating disorder. In addition to the 25 EDI-3 questions, this brief self-report form includes behavioral symptom questions to identify individuals with potential eating disorders or pathology. Referral indexes are used to identify individuals who have or are at risk for eating disorders.

Vicki Mark Permissions Specialist vmark parinc. Florida Avenue Lutz, FL All rights reserved. A degree from an accredited 4-year college or university in psychology, counseling, or a closely related field PLUS satisfactory completion of coursework in test interpretation, psychometrics and measurement theory, educational statistics, or a closely related area; OR license or certification from an agency that requires appropriate training and experience in the ethical and competent use of psychological tests.

Garner, Ph. Easily administered and scored, the EDI-3 yields 12 nonoverlapping scale scores and 6 composite scores that can be used to create clinically meaningful profiles that can be linked to treatment plans, specific interventions, and treatment monitoring. Reliably assess the developmental health skills and behaviour of children at the age of developmental transition from early development to school age in a holistic manner.

The EDI is a holistic assessment because it encompasses five major areas of child development. The EDI is a reliable and valid measurement tool of developmental status completed on individual children between 3.

After teachers complete the EDI on each individual child in their class, the results are grouped together to give a snap shot of how children are doing across schools, neighbourhoods, cities, or even provinces and countries. The EDI data are collected in the second half of the kindergarten year for two important reasons.

First, by that time, the teacher will have grown to know their students well and can easily and efficiently complete the instrument.

EDI Project Overview — A community-based data tool to measure child development Our objective is to work with communities to secure data on how their young children are doing. How Schools Can Benefit from Early Childhood Data Erikson provides coaching and technical assistance to use the accumulated data to better inform: strategic planning, proposal writing, and community visioning and planning.

Align and strengthen early childhood systems Identify strengths and gaps in early childhood programs and services Tailor supports for young children entering school Complement existing student assessments Shift problem-solving from individual to community solutions Assess community impact of child development over time. Educators and School Representatives Results help identify the strengths and challenges of the children in their schools, leading to targeted interventions for those children.

Parents and Community Leaders The data instigates community conversations that inform advocacy action planning. Elected Officials and Policymakers EDI data helps government plan equitable investments, inform policy, and evaluate program success over time. How Erikson Supports Community Partners Once we partner with a community, coalition, or school district, we provide support in many ways.

Greater East St. Louis: Early Learning Partnership. Kankakee County: Success by 6 Coalition. Oak Park: Collaboration for Early Childhood.

The best fitting model was, however, a 12 factor model M5 allowing all factors to correlate freely. To test the trustworthiness of the preceding model specifications for the covariance data, a random model M6 specifying the 90 EDI items to load on the 12 respective factors in an unsystematic fashion produced a poorer absolute and relative fit, as expected. All factors were allowed to correlate, as in model M5.

Summarized, the correlated 12 factor model received best support. However, a more parsimonious second-order model, which has a much simpler factor structure than the correlation model, is to prefer if a worsening of fit is not substantial, which it was not.

Two observations speak for favouring model M3. Firstly, the improvement in fit was larger when moving from model M2 to M3, rather than from model M3 to M4, especially in the control sample.

Secondly, an examination of the factor correlations among the three general factors indicated an extremely high correlation between the two psychological factors in model M4. Taken together eating problems should be summarized in two main scores to differentiate eating problems: one representing a risk factor and another representing a psychological disturbance score, according to the author i. Garner The factor loadings from second order factor analysis of model M3 are displayed in Table 5.

The relatively large number of chi-squares compared to degrees of freedom, is not reassuring either. Following an inspection of the modification indices, the mediocre fit appears related to several items showing hugely correlated residuals as well as significant factor side-loadings. Hence some of the EDI items do not have adequate psychometric properties.

The factor loadings for the second order two factor model M3 in Table 4 with risk and psychological disturbance as general factors accounting for the 12 primary factors. The two general factors were allowed to correlate. The figures show that the interoceptive deficits subscale is the best predictor across all diagnostic groups, followed by low self-esteem and personal alienation.

The bulimia subscale comes sixth overall, but is an excellent predictor of a diagnosis of BN with high sensitivity and specificity estimates. Table 6 provides an overview of sensitivity, specificity, likelihood ratios and diagnostic accuracy of the three best and the worst predictors within each diagnostic group.

Generally, increasing the cut off increases the specificity and reduces misclassification, but at the cost of increasing the number of false negatives patients not detected , which represents a more serious error.

Most ROC curves across the diagnostic groups are quite parallel over all levels of cut off scores, but with one notable exception. As expressed in Fig.

A low cut-off score starts in the right upper corner, going down the diagonal. Sensitivity, specificity, likelihood rates and diagnostic accuracy of the three best and the worst EDI-3 subscales for each diagnostic group. Cut-off scores within a range of -. Overall the new version of the Eating Disorder Inventory EDI-3 stands out as a psychodiagnostic assessment tool that may be used to capture eating problems.

Apart from one subscale with a medium effect size difference, all differences between the patient and the non clinical control group yielded high effect sizes, and even slightly higher than using the EDI-2 Clausen et al. Thus the discriminative validity is good, as is the case for internal consistency Table 3. The latter is even better in this study than in the original development of the EDI-3 Garner Thus, one argument for creating a new EDI-version i. Through the present large scale study, national norms have been successfully established.

For practical purposes, the implications from this study are that outside the US, the international norms Garner may be used for screening purposes when national norms are lacking. On the other hand, the lack of national clinical norms may lead to a more valid comparison to US than international data.

This certainly creates practical problems in doing epidemiological research, and may point to variations in how a psychological phenomenon e. The confirmatory factor analyses by and large supported the grouping of eating problems in two general factor scores, one assessing a risk component and the other assessing associated psychological disturbances. The model fit in the present study was actually better than what was presented in the EDI-3 manual Garner One reason for this may be that Garner based his analyses on twelve subscale sum scores rather than 90 item scores, as was done in the present study.

Still, the model fit was in the upper window of what is regarded as a minimal acceptable model approximation. This may be explained by the fact that several items had poor psychometric properties according to the modification indices provided by LISREL, showing hugely correlated error covariances and significant factor side-loadings. These items are thus ambiguous indicators of eating problems, and should be revised or removed in a future version of the EDI Identifying these items requires, however, an extensive item level analysis followed by a cross-validation on a holdout sample.

This is a task for another paper. In our study the sensitivity and specificity estimated make the bulimia subscale an excellent predictor of a BN-diagnosis. However, the overall purpose of the EDI-3 was to compose subscales with a conceptual content more congruent with domains identified by modern thinking about the nature of eating disorders Garner The ROC-analyses support the success of this purpose in the sense that the subscale interoceptive deficits is the best predictor across all diagnostic groups, followed by low self-esteem and personal alienation.

Previous studies have also found interoceptive awareness along with the three eating disorder specific subscales to discriminate between eating disorder patients and psychiatric controls Nevonen et al. Also, interoceptive issues are related to other psychological constructs of eating disorders like depression, perfectionism, and self directiveness Fassino et al. Hence, interoceptive deficits stand out as a concept with a high discriminative and construct validity related to eating disorders.

An important implication from the present findings is that the current use of the drive for thinness subscale as a screening tool in epidemiological studies is clearly not warranted any more. While people scoring high on drive for thinness may do this for good as well as bad reasons even unrelated to the pathology of eating disorders, disturbance in the accuracy of perception or recognition of bodily states is an important pathognomic sign of the specific eating disorder psychopathology, commonly seen as a failure to recognize signs of hunger Bruch Also noted by Bruch the all-pervading sense of ineffectiveness in patients with AN may be well captured by the EDI This kind of ineffectiveness may reflect a personality development attributed to a failure of confirmation of child initiated behaviour.