Providers' competencies positively affect personal recovery of involuntarily admitted patients with severe mental illness:A prospective observational study

Objective: There is limited research on the patient–provider relationship in inpatient settings. The purpose of this study was to measure the effect of mental healthcare providers’ recovery-promoting competencies on personal recovery in involuntarily admitted psychiatric patients with severe mental illness. Methods: In all, 127 Dutch patients suffering from a severe mental illness residing in a high-secure psychiatric hospital reported the degree of their personal recovery (translated Questionnaire about Processes of Recovery questionnaire (QPR)) and the degree of mental healthcare providers’ recovery-promoting competence (Recovery Promoting Relationship Scale (RPRS)) at two measurement points, 6 months apart. Analyses: (Mixed-effects) linear regression analysis was used to test the effect of providers’ recovery-promoting competence on personal recovery, while controlling for the following confounding variables: age, gender drug/alcohol problems, social relationships, activities of daily living, treatment motivation and medication adherence. Results: Analyses revealed a significant positive effect of providers’ recovery-promoting competencies on the degree of personal recovery (t = 8.4, p 4, p < .001). Conclusion: This study shows that recovery-promoting competencies of mental healthcare providers are positively associated with (a change in) personal recovery of involuntarily admitted patients. Further research is necessary on how to organize recovery-oriented care in inpatient settings and how to enhance providers’ competencies in a sustainable way

A New Acoustic-Based Pronunciation Distance Measure

We present an acoustic distance measure for comparing pronunciations, and apply the measure to assess foreign accent strength in American-English by comparing speech of non-native American-English speakers to a collection of native American-English speakers. An acoustic-only measure is valuable as it does not require the time-consuming and error-prone process of phonetically transcribing speech samples which is necessary for current edit distance-based approaches. We minimize speaker variability in the data set by employing speaker-based cepstral mean and variance normalization, and compute word-based acoustic distances using the dynamic time warping algorithm. Our results indicate a strong correlation of r = −0.71 (p < 0.0001) between the acoustic distances and human judgments of native-likeness provided by more than 1,100 native American-English raters. Therefore, the convenient acoustic measure performs only slightly lower than the state-of-the-art transcription-based performance of r = −0.77. We also report the results of several small experiments which show that the acoustic measure is not only sensitive to segmental differences, but also to intonational differences and durational differences. However, it is not immune to unwanted differences caused by using a different recording device

Dialect digitaal:nieuwe kansen voor streektaalgebruik en -onderzoek

Oratie uitgesproken door Prof.dr. Martijn Wieling op 04 maart 2022 bij de aanvaarding van het ambt van bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur aan de Faculteit der Letteren Rijksuniversiteit GroningenMartijn Wieling, bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur, vertelt in zijn oratie hoe hij en zijn collega’s van het Centrum Groninger Taal en Cultuur (CGTC) digitale technieken gebruiken om variatie en verandering in de Nedersaksische streektaal te onderzoeken.In zijn onderzoek gebruikt Wieling niet alleen bestaande dialectdata, maar verzamelen hij en zijn CGTC-collega’s ook nieuwe dialectdata. Deze data wordt opgeslagen in WoordWaark (www.woordwaark.nl), een online digitale taaldatabank voor het Gronings, opgezet en gecoördineerd door hoogleraar Goffe Jensma en ontwikkeld door dr. Wilbert Heeringa. Een van de projecten waarmee Wieling nieuwe dialectdata verzamelt is het door dr. Nanna Hilton geïnitieerde “Stemmen”-project. Deze website voorspelt waar een spreker vandaan komt op basis van zijn of haar dialectuitspraken.Van old noar jongEen tweede belangrijk project is “Van old noar jong”. In dit door Wieling gecoördineerde project wordt een onderwijsgame ontwikkeld waarmee kinderen op een toegankelijke manier kennis kunnen maken met het lokale Groningse dialect. Dit project wordt ondersteund door een Google Community Grant van 30.000 euro en is opgezet in samenwerking met hoogleraar Jensma en Goos Gosling Slotegraaf van Dorpsbelangen Zandeweer, Eppenhuizen en Doodstil.Een derde project waar Wieling zich voor inzet is de ontwikkeling van een Gronings tekst-naar-spraak systeem. In samenwerking met onder anderen Jenny van Doorn, RUG-hoogleraar dienstenmarketing, wordt onderzocht of zo’n systeem inzetbaar is in bijvoorbeeld de zorg. Een aanvullend doel van dit systeem is dat hiermee op termijn alle Groningse teksten binnen WoordWaark automatisch voorgelezen zullen kunnen worden

Analyzing dynamic phonetic data using generalized additive mixed modeling:a tutorial focusing on articulatory differences between L1 and L2 speakers of English

In phonetics, many datasets are encountered which deal with dynamic data collected over time. Examples include diphthongal formant trajectories and articulator trajectories observed using electromagnetic articulography. Traditional approaches for analyzing this type of data generally aggregate data over a certain timespan, or only include measurements at a fixed time point (e.g., formant measurements at the midpoint of a vowel). This paper discusses generalized additive modeling, a non-linear regression method which does not require aggregation or the pre-selection of a fixed time point. Instead, the method is able to identify general patterns over dynamically varying data, while simultaneously accounting for subject and item-related variability. An advantage of this approach is that patterns may be discovered which are hidden when data is aggregated or when a single time point is selected. A corresponding disadvantage is that these analyses are generally more time consuming and complex. This tutorial aims to overcome this disadvantage by providing a hands-on introduction to generalized additive modeling using articulatory trajectories from L1 and L2 speakers of English within the freely available R environment. All data and R code is made available to reproduce the analysis presented in this paper