Children in less-developed countries with mild to moderate disabilities often remain unidentified until school age. Delayed identification leads to less successful interventions for most children and risks secondary disabilities. The disability group at the Centre for International Child Health was funded to address this issue by developing a screening portfolio. The field testing of this portfolio is reported here. The results collected through quantitative analysis of the children brought for screening, and the fact that the field workers identified disabilities in children over the age of 2 years with over 82% accuracy when compared with professionals, suggests that health workers can be taught to use the portfolio effectively. Among younger children the accuracy is poorer. Field workers can additionally be trained to give advice to mothers and/or refer where appropriate. Equally important, results from focus group discussions with both health workers and parents, and questionnaires to health workers, demonstrate that both groups found the process clear and useful. Parents liked the process and found the advice materials helpful. Several health workers made comments about how the portfolio's use helped to develop positive attitudes towards disability and improved their own self-confidence.
Autonomous vehicle (AV) technology is developing rapidly. Level 3 automation assumes the user might need to respond to requests to retake control. Levels 4 (high automation) and 5 (full automation) do not require human monitoring of the driving task or systems [1]: the AV handles driving functions and makes decisions based on continuously updated information. A gradual switch in the role of the human within the vehicle from active controller to passive passenger comes with uncertainty in terms of trust, which will likely be a key barrier to acceptability, adoption and continued use [2]. Few studies have investigated trust in AVs and these have tended to use driving simulators with Level 3 automation [3,4]. The current study used both a driving simulator and autonomous road vehicle. Both were operating at Level 3 autonomy although did not require intervention from the user; much like Level 4 systems. Forty-six participants completed road circuits (UK-based) with both platforms. Trust was measured immediately after different types of turns at a priority T-junction, increasing in complexity: e.g., driving left or right out of a T-junction; turning right into a T-junction; presence of oncoming/crossing vehicles. Trust was high across platforms: higher in the simulator for some events and higher in the road AV for others. Generally, and often irrespective of platform, trust was higher for turns involving an oncoming/crossing vehicle(s) than without traffic, possibly because the turn felt more controlled as the simulator and road AVs always yielded, resulting in a delayed maneuver. We also found multiple positive relationships between trust in automation and technology, and trust ratings for most T-junction turn events across platforms. The assessment of trust was successful and the novel findings are important to those designing, developing and testing AVs with users in mind. Undertaking a trial of this scale is complex and caution should be exercised about over-generalizing the findings.
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