Jack shows thedifferencebetween simplemediation andmoderatedmediationMarydescribespotentialimpacts ofthe projectJack presentson logic modelsand how thiscan addressprogram needsThe groupparticipatesin a poll onfacilitatorsJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsAn exampleof the AERdashboardis presentedThe currentadverse eventmanagementsystem isshownLove betweenprocess focusedevaluations andquantitativemethods ishighlightedMary showshistoricaltrends inadverseeventsThe groupparticipatesin a poll onbarriersAn exampleof how modelmetrics worksis shownWe learn howallergens andmedicationsmay predictAERsTable withHoike toNaauao acrossdifferent playersis shownMary reviewsdifferent fivedifferent playersinvolved in theprojectThe futureadverse eventmanagementsystem isshownProgress isindicatedthroughweavingrelationshipsJames talksabout hisstory in thefoster homesystemMary talksabout thesysteminfrastructureand whereOEAIDD fits inDisney castleand dreamsfor theorganizationis presentedGeorge describesthe scope of theproject byidentifyingproblems andsolutionsAn exampleof topfeatures forthe modelsis shownJames talksabout issuesregardingusing digitaltoolsJack reviewshow the UHteam isinvolved inthe projectJack presentson how wemight mergedata withclinicaloutcomesMary showshistorical trendsin participantsreceivingservices inDDDData toWisdomtriangle isshownGeorgedescribes whatmachinelearning is witha pipeline figureDifferentassumptionsbuilt into alogic modelis presentedMary showshistorical trendsin adverseevents perparticipants inDDDTheproposedarchitectureof the systemis shownJack shows thedifferencebetween simplemediation andmoderatedmediationMarydescribespotentialimpacts ofthe projectJack presentson logic modelsand how thiscan addressprogram needsThe groupparticipatesin a poll onfacilitatorsJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsAn exampleof the AERdashboardis presentedThe currentadverse eventmanagementsystem isshownLove betweenprocess focusedevaluations andquantitativemethods ishighlightedMary showshistoricaltrends inadverseeventsThe groupparticipatesin a poll onbarriersAn exampleof how modelmetrics worksis shownWe learn howallergens andmedicationsmay predictAERsTable withHoike toNaauao acrossdifferent playersis shownMary reviewsdifferent fivedifferent playersinvolved in theprojectThe futureadverse eventmanagementsystem isshownProgress isindicatedthroughweavingrelationshipsJames talksabout hisstory in thefoster homesystemMary talksabout thesysteminfrastructureand whereOEAIDD fits inDisney castleand dreamsfor theorganizationis presentedGeorge describesthe scope of theproject byidentifyingproblems andsolutionsAn exampleof topfeatures forthe modelsis shownJames talksabout issuesregardingusing digitaltoolsJack reviewshow the UHteam isinvolved inthe projectJack presentson how wemight mergedata withclinicaloutcomesMary showshistorical trendsin participantsreceivingservices inDDDData toWisdomtriangle isshownGeorgedescribes whatmachinelearning is witha pipeline figureDifferentassumptionsbuilt into alogic modelis presentedMary showshistorical trendsin adverseevents perparticipants inDDDTheproposedarchitectureof the systemis shown

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