James talksabout hisstory in thefoster homesystemDisney castleand dreamsfor theorganizationis presentedJack presentson how wemight mergedata withclinicaloutcomesLove betweenprocess focusedevaluations andquantitativemethods ishighlightedJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsMary showshistoricaltrends inadverseeventsGeorge describesthe scope of theproject byidentifyingproblems andsolutionsProgress isindicatedthroughweavingrelationshipsTheproposedarchitectureof the systemis shownAn exampleof the AERdashboardis presentedData toWisdomtriangle isshownGeorgedescribes whatmachinelearning is witha pipeline figureAn exampleof how modelmetrics worksis shownAn exampleof topfeatures forthe modelsis shownMary talksabout thesysteminfrastructureand whereOEAIDD fits inMary showshistorical trendsin adverseevents perparticipants inDDDWe learn howallergens andmedicationsmay predictAERsJames talksabout issuesregardingusing digitaltoolsDifferentassumptionsbuilt into alogic modelis presentedThe futureadverse eventmanagementsystem isshownTable withHoike toNaauao acrossdifferent playersis shownThe currentadverse eventmanagementsystem isshownJack presentson logic modelsand how thiscan addressprogram needsMary showshistorical trendsin participantsreceivingservices inDDDMarydescribespotentialimpacts ofthe projectJack reviewshow the UHteam isinvolved inthe projectMary reviewsdifferent fivedifferent playersinvolved in theprojectThe groupparticipatesin a poll onbarriersJack shows thedifferencebetween simplemediation andmoderatedmediationThe groupparticipatesin a poll onfacilitatorsJames talksabout hisstory in thefoster homesystemDisney castleand dreamsfor theorganizationis presentedJack presentson how wemight mergedata withclinicaloutcomesLove betweenprocess focusedevaluations andquantitativemethods ishighlightedJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsMary showshistoricaltrends inadverseeventsGeorge describesthe scope of theproject byidentifyingproblems andsolutionsProgress isindicatedthroughweavingrelationshipsTheproposedarchitectureof the systemis shownAn exampleof the AERdashboardis presentedData toWisdomtriangle isshownGeorgedescribes whatmachinelearning is witha pipeline figureAn exampleof how modelmetrics worksis shownAn exampleof topfeatures forthe modelsis shownMary talksabout thesysteminfrastructureand whereOEAIDD fits inMary showshistorical trendsin adverseevents perparticipants inDDDWe learn howallergens andmedicationsmay predictAERsJames talksabout issuesregardingusing digitaltoolsDifferentassumptionsbuilt into alogic modelis presentedThe futureadverse eventmanagementsystem isshownTable withHoike toNaauao acrossdifferent playersis shownThe currentadverse eventmanagementsystem isshownJack presentson logic modelsand how thiscan addressprogram needsMary showshistorical trendsin participantsreceivingservices inDDDMarydescribespotentialimpacts ofthe projectJack reviewshow the UHteam isinvolved inthe projectMary reviewsdifferent fivedifferent playersinvolved in theprojectThe groupparticipatesin a poll onbarriersJack shows thedifferencebetween simplemediation andmoderatedmediationThe groupparticipatesin a poll onfacilitators

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