Sentence examples for information reinforcement from inspiring English sources

Finally, the diabetes treatment team can also assist doctor-patient communication through consistent messages, repetition of information, reinforcement and feedback of self-care behaviors, and greater availability to problem-solve concerns about self-care (40).

This ongoing informal reinforcement by nurses is invaluable for continuity of care, especially as patients often forget information over time.

Results show that positive recovery activities is an optimal solution in green SC among single strategies; simulated scenarios alleviate the BE largely especially the combination of higher recovery ratio and information transparency reinforcement.

The application of LE-based metypicallyically results in determination of a slip surface corresponding to the lowest attained Safety Factor (SF), known as the Factor of Safety (Fs); however, it yields little information about reinforcement loading or connection load.

Learning styles specific information and reinforcement of consistent messages will ensure correct understanding.

Since YAMs have high usage of mobile phones and text messaging [ 21], the mobile phone would be a good vehicle to provide information and reinforcement regarding healthy behaviour.

The recruitment strategies included providing information and reinforcement of the potential benefits of the intervention to family physicians and older adults, referrals through the family physicians and dissemination of printed materials.

For example, the advantage of a school-operated initiative is logistical (established channels of information, peer reinforcement and large numbers of girls in the same place at the same time).

Considering the central role of the MB in sensory integration and memory-guided behavior (Aso et al., 2014b), recurrent computation between learned information and reinforcement signals might be a fundamental principle of memory processing.

Assigning a real thickness to parts of these layers, which is controlled by sensitivity information, creates local reinforcement doublers.

Reinforcement learning algorithms such as Q-learning are particularly efficient in applications where reinforcement information (i.e., cost or reward) is provided after an action is performed in the environment [11].