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The expected outcome of a successful social worse pathway intervention is clinical performance improvement.
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## Message templates
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Message templates represent a possible motivational message that a precision feedback system can send. There is one message template that the precision feedback system can currently access for this vignette:
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The `Not Top Performer`[<sub>(GH)</sub>](https://github.com/Display-Lab/knowledge-base/blob/main/message_templates/not_top_performer.json) message reads:
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1.[Social comparator element <sub>(BP)</sub>](https://bioportal.bioontology.org/ontologies/PSDO?p=classes&conceptid=http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FPSDO_0000045)
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2.[Negative performance gap set <sub>(BP)</sub>](https://bioportal.bioontology.org/ontologies/PSDO?p=classes&conceptid=http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FPSDO_0000116)
The first stage of the pipeline is a script called Bitstomach. This script analyzes recipient's performance data and generates features called **annotations**. These annotations describe features of the performance data such as performance trends over time, or how performance measures relate to other values like peer performance benchmarks and institutional performance goals.
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### Message Generation and Delivery (Pictoralist)
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The fifth and final stage of the pipeline generates visual representations of the selected message based on the recipient's performance data, and sends this output off for delivery to the recipient.
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The script takes in the selected message and performance data of the recipient as inputs. It then uses the recipient's display preferences to generate a figure that visualize's the reciepients performance over time related to the given measure and causal pathway. It returns the figure as an image encoded as a base64 string, alongside other elements of the precision feedback message. The precision feedback email is then created using this figure, the necessary text data, and is then sent to the recipient.
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The script takes in the selected message and performance data of the recipient as inputs. It then uses the recipient's display preferences to generate a figure that visualize's the reciepients performance over time related to the given measure and causal pathway. It returns the figure as an image encoded as a base64 string, alongside other elements of the precision feedback message. The precision feedback email is then created using this figure, the necessary text data, and is then sent to the recipient.
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