Table withHoike toNaauao acrossdifferent playersis shownThe currentadverse eventmanagementsystem isshownThe groupparticipatesin a poll onfacilitatorsAn exampleof how modelmetrics worksis shownJames talksabout issuesregardingusing digitaltoolsThe futureadverse eventmanagementsystem isshownJack presentson how wemight mergedata withclinicaloutcomesJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsTheproposedarchitectureof the systemis shownWe learn howallergens andmedicationsmay predictAERsMary showshistoricaltrends inadverseeventsData toWisdomtriangle isshownGeorgedescribes whatmachinelearning is witha pipeline figureProgress isindicatedthroughweavingrelationshipsMary reviewsdifferent fivedifferent playersinvolved in theprojectDisney castleand dreamsfor theorganizationis presentedJames talksabout hisstory in thefoster homesystemMary showshistorical trendsin adverseevents perparticipants inDDDThe groupparticipatesin a poll onbarriersAn exampleof the AERdashboardis presentedLove betweenprocess focusedevaluations andquantitativemethods ishighlightedMary talksabout thesysteminfrastructureand whereOEAIDD fits inMarydescribespotentialimpacts ofthe projectJack reviewshow the UHteam isinvolved inthe projectAn exampleof topfeatures forthe modelsis shownGeorge describesthe scope of theproject byidentifyingproblems andsolutionsJack shows thedifferencebetween simplemediation andmoderatedmediationMary showshistorical trendsin participantsreceivingservices inDDDDifferentassumptionsbuilt into alogic modelis presentedJack presentson logic modelsand how thiscan addressprogram needsTable withHoike toNaauao acrossdifferent playersis shownThe currentadverse eventmanagementsystem isshownThe groupparticipatesin a poll onfacilitatorsAn exampleof how modelmetrics worksis shownJames talksabout issuesregardingusing digitaltoolsThe futureadverse eventmanagementsystem isshownJack presentson how wemight mergedata withclinicaloutcomesJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsTheproposedarchitectureof the systemis shownWe learn howallergens andmedicationsmay predictAERsMary showshistoricaltrends inadverseeventsData toWisdomtriangle isshownGeorgedescribes whatmachinelearning is witha pipeline figureProgress isindicatedthroughweavingrelationshipsMary reviewsdifferent fivedifferent playersinvolved in theprojectDisney castleand dreamsfor theorganizationis presentedJames talksabout hisstory in thefoster homesystemMary showshistorical trendsin adverseevents perparticipants inDDDThe groupparticipatesin a poll onbarriersAn exampleof the AERdashboardis presentedLove betweenprocess focusedevaluations andquantitativemethods ishighlightedMary talksabout thesysteminfrastructureand whereOEAIDD fits inMarydescribespotentialimpacts ofthe projectJack reviewshow the UHteam isinvolved inthe projectAn exampleof topfeatures forthe modelsis shownGeorge describesthe scope of theproject byidentifyingproblems andsolutionsJack shows thedifferencebetween simplemediation andmoderatedmediationMary showshistorical trendsin participantsreceivingservices inDDDDifferentassumptionsbuilt into alogic modelis presentedJack presentson logic modelsand how thiscan addressprogram needs

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