Training assessment

This page is a knowledge crystal of subtype assessment. The page identifier is Op_en5677
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This is a training assessment about an imaginary, simple case. The purpose is to illustrate assessment functionalities.

Scope

Question

What decisions are worth implementing in the training assessment?

Boundaries

• Time: Year 2012 - 2020

Scenarios

• Factory can reduce emissions, or continue business as usual.
• School can increase health education, decrease it to save money, or continue business as usual.

Intended users

• Anyone who wants to learn to make open assessments.

Participants

• Main participants:
  • YMAL,
  • Summer workers of YMAL in 2012,
  • Participants of Decision analysis and risk management 2013

Answer

Results

Not yet available.

Rationale

The causal diagram for the training assessment.

Assessment-specific data

Decisions

Decisions(-)

Obs	Decisionmaker	Decision	Option	Variable	Cell	Change	Unit	Amount	Description
1	Factory	Cleaning_policy	BAU	exposure	Year:2020	Multiply	-	1
2	Factory	Cleaning_policy	Reduce emissions	exposure	Year:2020	Multiply	-	0.5
3	Factory	Cleaning_policy	BAU	costs		Add	€	0
4	Factory	Cleaning_policy	Reduce emissions	costs	Year:2020;Expenditure:Cleaning equipment use	Add	€	30000-40000
5	City of Kuopio	Health_promotion	BAU	health_impact		Multiply	-	1
6	City of Kuopio	Health_promotion	Increase health education	health_impact		Multiply	-	0.9
7	City of Kuopio	Health_promotion	BAU	costs		Add	€	0
8	City of Kuopio	Health_promotion	Increase health education	costs	Year:2020;Expenditure:Health promotion campaign	Add	€	10000

Variables

• exposure: Training exposure
• health_impact: Training health impact
• costs: Training costs

Analyses

• Decision analysis on each policy: Which option minimises the health risks?
• Value of information (VOI) analysis for each policy about the major variables in the model and the total VOI.

Calculations

What is the unit cost of one case of disease?: + Show code- Hide code library(OpasnetUtils) library(ggplot2) DecisionTableParser(opbase.data("Op_en5677", subset = "Decisions")) # [[Training assessment]] objects.latest("Op_en5675", code_name = "initiate") # [[Training health impact]] health_impact objects.latest("Op_en5871", code_name = "initiate") # [[Training costs]] costs unitcost <- Ovariable("unitcost", data = data.frame(Result = unitcost)) costs <- CheckDecisions(EvalOutput(costs)) costs2 <- oapply(unkeep(costs, sources = TRUE), cols ="Expenditure", FUN = sum) cat("Summary of exposures\n") oprint(summary(exposure)) cat("Summary of health impacts\n") oprint(summary(health_impact), digits = 0) cat("Summary of all costs\n") oprint(summary(costs2), digits = 0) ggplot(exposure@output, aes(x = exposureResult, colour = Year)) + geom_density() + theme_gray(base_size = 24) + facet_grid(Cleaning_policy ~ .) ggplot(health_impact@output, aes(x = health_impactResult, colour = Year)) + geom_density() + theme_gray(base_size = 24) + facet_grid(Cleaning_policy ~ Health_promotion) ggplot(subset(costs2@output, Year == 2020), aes(x = costsResult, colour = Payer)) + geom_density() + theme_gray(base_size = 24) + facet_grid(Cleaning_policy ~ Health_promotion) ggplot(subset(costs2@output, Year == 2020), aes(x = Payer, weight = costsResult / openv$N, fill = Payer)) + geom_bar() + theme_gray(base_size = 24) + facet_grid(Cleaning_policy ~ Health_promotion)

Explanations of the code

 <rcode graphics=1 
 	variables="name:unitcost|description:What is the unit cost of one case of disease?|type:text|default:100"
 >

The tag above starts the R code. It turns on the graphics so that graphs can be drawn. It also asks the user to give a value for the variable unitcost.

library(OpasnetUtils)
library(ggplot2)

In the beginning of a code, packages that are needed are loaded. These are the two most commonly used. OpasnetUtils is for all Opasnet functionalities, ggplot2 is for cool graphs.

DecisionTableParser(opbase.data("Op_en5677", subset = "Decisions")) # [[Training assessment]]

This code defines from where to take the assessment-specific decisions. In this case, they are on the page Training assessment, in a table called Decisions. The page has identifier Op_en5677, which can be found from the metadata box, which is typically in the top right corner of a page.

objects.latest("Op_en5675", code_name = "initiate") # [[Training health impact]] health_impact
objects.latest("Op_en5871", code_name = "initiate") # [[Training costs]] costs

These lines fetch ready-made ovariables that are needed in this assessment. The code defining these ovariables are located on the pages mentioned. Note that # is the comment character and everything after it is ignored in the model; so it is used to put explanations for the reader within the code.

unitcost <- Ovariable("unitcost", data = data.frame(Result = unitcost))

Unitcost was asked from the user as a value, and now it is converted to an ovariable.

costs <- EvalOutput(costs)

costs2 <- oapply(unkeep(costs, sources = TRUE), cols ="Expenditure", FUN = sum)

Because all ovariables are connected in the model, it is enough to evaluate the last one. The model will automatically evaluate all ovariables that are causally upstream. Also, the decisions that apply to this assessment will change the results of the ovariables as necessary. Ovariable costs2 is used to summarise contents of costs for easier presentation.

cat("Summary of health impacts\n")

Function cat is used to print outputs for the user.

oprint(summary(health_impact), digits = 0)

Function oprint is used to print tables for the user.

cat("Summary of all costs\n")

oprint(summary(costs2), digits = 0)

ggplot(exposure@output, aes(x = exposureResult, colour = Year)) + geom_density() +
	theme_gray(base_size = 24) +
	facet_grid(Cleaning_policy ~ .)

Function ggplot is very effective in creating all kinds of graphs with just a few lines. It is really worth learning.

ggplot(health_impact@output, aes(x = health_impactResult, colour = Year)) + geom_density() +
	theme_gray(base_size = 24) +
	facet_grid(Cleaning_policy ~ Health_promotion)

ggplot(subset(costs2@output, Year == 2020), aes(x = costsResult, colour = Payer)) + geom_density() +
	theme_gray(base_size = 24) +
	facet_grid(Cleaning_policy ~ Health_promotion)

 </rcode>

The code must always end with this tag.

See also

• Descriptions of a previous structure
• ----#: . Päätöksenteon sokea piste: se mitä ihmiset eivät näe mutta eivät myöskään huomaa etteivät näe. Kuitenkin tutkimalla sitä mitä mitä ihmiset eivät näe saadaan selville asioita sokeasta pisteesta. Ymmärtämällä sokeaa pistettä voidaan keksiä asioita jotka järjestelmällisesti jäävät huomaamatta ja asioita, joilla voidaan korjata järjestelmällisiä puutteita. Avoin arviointi on tämmöinen päätöksenteon järjestelmällisten puutteiden korjausmekanismi. --Jouni 08:55, 1 May 2012 (EEST) (type: truth; paradigms: science: comment)

Related files

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