Mary showshistorical trendsin participantsreceivingservices inDDDDifferentassumptionsbuilt into alogic modelis presentedThe groupparticipatesin a poll onbarriersJames talksabout hisstory in thefoster homesystemAn exampleof how modelmetrics worksis shownMarydescribespotentialimpacts ofthe projectJack shows thedifferencebetween simplemediation andmoderatedmediationJack presentson logic modelsand how thiscan addressprogram needsJack presentson how wemight mergedata withclinicaloutcomesJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsJack reviewshow the UHteam isinvolved inthe projectMary showshistoricaltrends inadverseeventsLove betweenprocess focusedevaluations andquantitativemethods ishighlightedWe learn howallergens andmedicationsmay predictAERsThe currentadverse eventmanagementsystem isshownTable withHoike toNaauao acrossdifferent playersis shownData toWisdomtriangle isshownTheproposedarchitectureof the systemis shownThe futureadverse eventmanagementsystem isshownJames talksabout issuesregardingusing digitaltoolsDisney castleand dreamsfor theorganizationis presentedProgress isindicatedthroughweavingrelationshipsMary showshistorical trendsin adverseevents perparticipants inDDDAn exampleof the AERdashboardis presentedThe groupparticipatesin a poll onfacilitatorsGeorge describesthe scope of theproject byidentifyingproblems andsolutionsGeorgedescribes whatmachinelearning is witha pipeline figureMary talksabout thesysteminfrastructureand whereOEAIDD fits inAn exampleof topfeatures forthe modelsis shownMary reviewsdifferent fivedifferent playersinvolved in theprojectMary showshistorical trendsin participantsreceivingservices inDDDDifferentassumptionsbuilt into alogic modelis presentedThe groupparticipatesin a poll onbarriersJames talksabout hisstory in thefoster homesystemAn exampleof how modelmetrics worksis shownMarydescribespotentialimpacts ofthe projectJack shows thedifferencebetween simplemediation andmoderatedmediationJack presentson logic modelsand how thiscan addressprogram needsJack presentson how wemight mergedata withclinicaloutcomesJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsJack reviewshow the UHteam isinvolved inthe projectMary showshistoricaltrends inadverseeventsLove betweenprocess focusedevaluations andquantitativemethods ishighlightedWe learn howallergens andmedicationsmay predictAERsThe currentadverse eventmanagementsystem isshownTable withHoike toNaauao acrossdifferent playersis shownData toWisdomtriangle isshownTheproposedarchitectureof the systemis shownThe futureadverse eventmanagementsystem isshownJames talksabout issuesregardingusing digitaltoolsDisney castleand dreamsfor theorganizationis presentedProgress isindicatedthroughweavingrelationshipsMary showshistorical trendsin adverseevents perparticipants inDDDAn exampleof the AERdashboardis presentedThe groupparticipatesin a poll onfacilitatorsGeorge describesthe scope of theproject byidentifyingproblems andsolutionsGeorgedescribes whatmachinelearning is witha pipeline figureMary talksabout thesysteminfrastructureand whereOEAIDD fits inAn exampleof topfeatures forthe modelsis shownMary reviewsdifferent fivedifferent playersinvolved in theproject

OEAIDD Data Party BINGO - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. Mary shows historical trends in participants receiving services in DDD
  2. Different assumptions built into a logic model is presented
  3. The group participates in a poll on barriers
  4. James talks about his story in the foster home system
  5. An example of how model metrics works is shown
  6. Mary describes potential impacts of the project
  7. Jack shows the difference between simple mediation and moderated mediation
  8. Jack presents on logic models and how this can address program needs
  9. Jack presents on how we might merge data with clinical outcomes
  10. James discusses ways that we might address issues using digital tools
  11. Jack reviews how the UH team is involved in the project
  12. Mary shows historical trends in adverse events
  13. Love between process focused evaluations and quantitative methods is highlighted
  14. We learn how allergens and medications may predict AERs
  15. The current adverse event management system is shown
  16. Table with Hoike to Naauao across different players is shown
  17. Data to Wisdom triangle is shown
  18. The proposed architecture of the system is shown
  19. The future adverse event management system is shown
  20. James talks about issues regarding using digital tools
  21. Disney castle and dreams for the organization is presented
  22. Progress is indicated through weaving relationships
  23. Mary shows historical trends in adverse events per participants in DDD
  24. An example of the AER dashboard is presented
  25. The group participates in a poll on facilitators
  26. George describes the scope of the project by identifying problems and solutions
  27. George describes what machine learning is with a pipeline figure
  28. Mary talks about the system infrastructure and where OEAIDD fits in
  29. An example of top features for the models is shown
  30. Mary reviews different five different players involved in the project