Preference Assessment of Teacher Behaviors for Students with Autism and resulting Behaviors

With the rising prevalence of autism spectrum disorder (ASD), studies must seek to evaluate enriched, innovative teaching experiences for this growing population. Preference assessments are widely used to evaluate favored edibles, objects and activities for children with ASD. Less research has been conducted regarding children’s preferences for staff. Since children with ASD spend a large part of their day with teachers in their schools or therapeutic environments, it seems fitting that they would develop a preference for teachers. If students develop preferences for teachers, it would also make sense that these preferences may have an effect on the student’s behavior. Two students with autism and their everyday teachers participated in this study. A multiple-stimulus without replacement preference assessment using pictorial presentation established a hierarchy for the student’s preference for particular teachers. Teachers were ranked as either high or low preference for the student. The effect of each student’s preference for a particular teacher on the student’s behavior was compared in an alternating treatment design. The purpose of this study was to demonstrate a relation between preference for particular teachers and student task performance while completing a task in which they had previously displayed problem behavior. Participants worked in a 1:1 instructional setting for 5 minute intervals. A 10-second partial- interval recording method was used to document the occurrence of problem behavior. As hypothesized, both students engaged in lower rates of problem behavior while working with their highest preferred teacher. Further research is needed to elucidate the cause of this relationship

Geographical Heterogeneity of Multiple Sclerosis Prevalence in France.

INTRODUCTION:Geographical variation in the prevalence of multiple sclerosis (MS) is controversial. Heterogeneity is important to acknowledge to adapt the provision of care within the healthcare system. We aimed to investigate differences in prevalence of MS in departments in the French territory. METHODS:We estimated MS prevalence on October 31, 2004 in 21 administrative departments in France (22% of the metropolitan departments) by using multiple data sources: the main French health insurance systems, neurologist networks devoted to MS and the Technical Information Agency of Hospitalization. We used a spatial Bayesian approach based on estimating the number of MS cases from 2005 and 2008 capture-recapture studies to analyze differences in prevalence. RESULTS:The age- and sex-standardized prevalence of MS per 100,000 inhabitants ranged from 68.1 (95% credible interval 54.6, 84.4) in Hautes-Pyrénées (southwest France) to 296.5 (258.8, 338.9) in Moselle (northeast France). The greatest prevalence was in the northeast departments, and the other departments showed great variability. DISCUSSION:By combining multiple data sources into a spatial Bayesian model, we found heterogeneity in MS prevalence among the 21 departments of France, some with higher prevalence than anticipated from previous publications. No clear explanation related to health insurance coverage and hospital facilities can be advanced. Population migration, socioeconomic status of the population studied and environmental effects are suspected

Administrative departments in France (coding number) ranked by latitude.

<p>Yellow: <b>Northeast region (NE) Lorraine</b>: Moselle (57); Meuse (55); Meurthe-et-Moselle (54); Vosges (88) Pink: <b>Northwest region (NW) Bretagne</b>: Côtes-d’Armor (22); Finistère (29); Ille-et-Vilaine (35); Morbihan (56) Green<b>: Center region (C) Auvergne</b>: Allier (03); Haute-Loire (43); Puy-de-Dôme (63); Cantal (15) Blue<b>: Southeast region (SE) Rhône-Alpes</b>: Rhône (69) Orange<b>: Southwest region (SW) Midi-Pyrénées</b>: Lot (46); Aveyron (12); Tarn-et-Garonne (82); Tarn (81); Gers (32); Haute-Garonne (31); Hautes-Pyrénées (65); Ariège (09).</p

Relative risk using the department with the lowest shares of overlap, Moselle ^a.

<p>Relative risk using the department with the lowest shares of overlap, Moselle <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167556#t006fn001" target="_blank">^a</a>.</p

Number of multiple sclerosis (MS) cases obtained from each data source for the year 2004.

<p>Number of multiple sclerosis (MS) cases obtained from each data source for the year 2004.</p

Relative risk of MS for each department compared to the mean prevalence for all departments ^a.

<p>Relative risk of MS for each department compared to the mean prevalence for all departments <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167556#t004fn001" target="_blank">^a</a>.</p

Standardized MS prevalence per 100,000 inhabitants for the 21 departments under study.

<p>Standardized MS prevalence per 100,000 inhabitants for the 21 departments under study.</p

Estimation of age- and sex-standardized MS prevalence ^a per 100,000 inhabitants in European and world populations.

<p>Estimation of age- and sex-standardized MS prevalence ^a per 100,000 inhabitants in European and world populations.</p

Relative risk using the department with the highest shares of overlap, Haute-Garonne^a.

<p>Relative risk using the department with the highest shares of overlap, Haute-Garonne<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167556#t005fn001" target="_blank">^a</a>.</p