Abstract While statistical word learning has been the focus of many studies on monolinguals, it has received little attention in bilinguals. The results of existing studies on statistical word learning in bilinguals are inconsistent, with some research reporting a bilingual advantage over monolinguals but others finding no difference between groups. Thus, our study investigated statistical learning using the cross-situational word learning (CSWL) paradigm between two groups: English–German bilinguals and English monolinguals. Participants (N = 201) learned 1 : 1 mappings (one word maps onto one object) and 1 : 2 mappings (one word maps onto two objects). In contrast to previous studies, we measured learning continuously and analysed trial-by-trial behaviour closely to understand fine-grained learning differences across language groups. Against our predictions, monolinguals generally outperformed bilinguals in word learning regardless of mapping type, and trial-by-trial analyses did not reveal differences in learning as expected. Moreover, language balancedness of bilinguals (operationalized via language entropy) did not account for differences in CSWL performance. These results suggest that any existing word learning advantages may be highly paradigm- and population-specific.

Background: Dental caries is a common chronic disease in children, yet large-scale surveillance is limited by the resource demands of clinical examinations. Digital tools like intraoral scanners may offer a scalable alternative for caries detection, enabling more efficient data collection for research and population-level oral health monitoring. Objective: To assess the diagnostic agreement between visual examination and on-screen assessment of 3D models generated by an intraoral scanner (IOS) in colour and supplemented with fluorescence for caries detection in primary teeth. Methods: Children participating in a clinical trial (n=216, mean age 5.6 years) underwent visual examination using the International Caries Detection and Assessment System (ICDAS) and intraoral scanning using the TRIOS 4 IOS (3Shape TRIOS A/S Denmark). Four trained registered dental practitioners independently assessed each participant’s 3D models in colour, then supplemented with fluorescence, using a previously validated ICDAS index modified for on-screen assessments of 3D models. All 3D models were assessed again four weeks later. The time taken for intraoral scanning and on-screen assessment was recorded. Multilevel logistic regression was used to estimate and compare the likelihood of detecting caries between methods, and Bland–Altman plots were used to visualise agreement. Analyses were performed at the initial (ICDAS>01), moderate (ICDAS>03), and extensive (ICDAS>05) dental caries thresholds. Intraclass Correlation Coefficient (ICC) estimated method agreement and examiner reliability. Results: 8,209 visible primary tooth coronal surfaces were analysed. The likelihood of detecting caries using colour assessment of 3D models was similar to visual examination at all disease thresholds: initial (odds ratio 1.1, 95% confidence interval (CI) 1.0–1.3), moderate (0.9, 0.7-1.1) and extensive (1.0, 0.7–1.3). When colour assessments were supplemented with fluorescence, the likelihood of detecting caries was 30% higher at the initial threshold relative to visual examination (1.3, 1.1-1.5) and similar at the moderate (0.9, 0.7-1.1) and extensive thresholds (0.9, 0.7-1.3). Bland-Altman plots showed a high level of agreement at both moderate and extensive thresholds. Examiner reliability using intraoral scans ranged from good to excellent. Conclusions: On-screen assessment of 3D models in colour shows greatest agreement with visual examination for caries detection at all disease thresholds. Clinical Trial: Australian New Zealand Clinical Trials Registry ACTRN12622001237774

Background: The global, nearly universal penetration of mobile phones has offered novel opportunities for delivering health services and communicating health-related information to individuals. A low-cost system that facilitates autonomous communication with individuals via mobile phones holds potential for expanding the reach of health messaging and service delivery in settings with human resource and infrastructure limitations. Objective: We sought to design a flexible, low-code system using open-source software that could be adapted to different contexts and technical environments and accommodate a wide range of automation needs. We report on key details of the mobile phone-based appointment reminder and incentive system (mParis), document its use, review implementation challenges and adaptations to address these challenges in the context of a quasi-randomized trial of mobile phone-based reminders and incentives as means of increasing the timeliness of routine childhood vaccinations in Tanzania, and outline other use cases that highlight the versatility of the system. Methods: The mParis instance described in this manuscript, which is hosted in Tanzania, sent automated, individualized vaccination reminders to the mobile phones of mothers of young children. Process workflows, based on the national childhood vaccination schedule of Tanzania, were programmed into mParis. Reminders for vaccinations due at ages 6, 10, and 14 weeks were sent 7 days and 1 day prior to and 14 days after each vaccination due date. A subset of messages included financial incentive offers to mothers for the timely vaccination of their children. We report on implementation outcomes, challenges, and adaptations to address these challenges. Results: Between August and December 2017, 412 pregnant women were enrolled into the trial. After mothers reported the birth of their children, individualized vaccination reminder messages were sent for vaccination due dates between January and July 2018. From March 2018, messages contained financial incentive offers. Of 1,397 messages sent, 1,122 SMS (80.3%) were recorded as delivered, 249 (18.8%) as expired and resent, 23 (1.6%) as failed, and 3 (0.2%) as sent but lacking delivery confirmation. 633 messages (45.3%) contained tiered incentive offers. Of 173 women who received at least one message, 67 (38.7%) were sent reminders only; 106 women (61.3%) were sent at least one incentivized message. Numerous challenges were encountered during the system’s implementation, despite its deliberate design to accommodate basic problems such as intermittent internet access and power failures. Continuous adaptation to increase the resilience of the system, complemented by limited backstops, resulted in a successful deployment. Conclusions: mParis’ open-source nature, auditability, and ability to autonomously execute algorithms in a low-resource setting with frequent infrastructure challenges suggest favorable prospects to automate health communication in a wide range of settings. mParis’ use in multiple other health-related research studies demonstrates its versatility and ability to accommodate challenges that may be encountered in diverse other contexts. Clinical Trial: ClinicalTrials.gov: NCT03252288