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" " T?d @?"|I # c BC(DE F(y @( T < HHN0XB@"! * $ c B<CDE,F6 @ $0N< N/$_@`"I % 68: #" `s ` : `*0  & 6`: #" `s  : b*0  ' 0: " `   : W#Click to edit Master subtitle style$ $ ( <h: "Z : T Click to edit Master title style! ! ) 6: #" `s ` : b*0 T  <2޽h ? +T3f3f80___PPT10. / 0 *(    0*w P   w X*0    00w    w Z*0  d  c $ ?  w  0.w  @ w RClick to edit Master text styles Second level Third level Fourth level Fifth level!     S  69w `P  w X*0    6>w `  w Z*0  H  0޽h ? ̙3380___PPT10.9 b 0(`\(  \ \ 0(w P   y `*0  \ 0`w    y b*0  \ 6H1y `P  y `*0  \ 6p7y `  y b*0 H \ 0޽h ? ̙3380___PPT10.9?` 0 0v(  x  c $:(Z : r  S Į:' `   : Z  c 2Ablue-w  <:Q GW v:Carl Thompson Department of Health, Senior Research Fellow ;0;H  0޽h ? ̙33y___PPT10Y+D=' y= @B +  0 .&`(  r  S P:%  `  : J  C "A lens  <Ԃ: N ecology  0 H  0޽h ? +T3f3fy___PPT10Y+D=' y= @B +J  0 (  R  s *A ?H  0޽h ? +T3f3fp 0  P(  x  <A ?? H  0޽h ? +T3f3f   0 rj(  r  S |t:% 0  :   <A !?? P8 !$D  0  <A ?? P8 $D 0H  0޽h ? +T3f3f6 . ___PPT10 +[D ' = @B D ' = @BA?%,( < +O%,( < +Dn' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+#.<*%(D' =+4 8?dCB0-#ppt_w/2BCB#ppt_xB*Y3>B ppt_x<*D' =+4 8?\CB#ppt_yBCB#ppt_yB*Y3>B ppt_y<*Dn' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+#.<*%(D' =+4 8?dCB1+#ppt_w/2BCB#ppt_xB*Y3>B ppt_x<*D' =+4 8?\CB#ppt_yBCB#ppt_yB*Y3>B ppt_y<*+   0 rj(  r  S ֍% 0     <A ??0 8 $D 0  <A ??0j 8 $D  0H  0޽h ? +T3f3f6 . ___PPT10 +?D ' = @B D ' = @BA?%,( < +O%,( < +Dn' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+#.<*%(D' =+4 8?dCB0-#ppt_w/2BCB#ppt_xB*Y3>B ppt_x<*D' =+4 8?\CB#ppt_yBCB#ppt_yB*Y3>B ppt_y<*Dn' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+#.<*%(D' =+4 8?dCB1+#ppt_w/2BCB#ppt_xB*Y3>B ppt_x<*D' =+4 8?\CB#ppt_yBCB#ppt_yB*Y3>B ppt_y<*+  0  *(  r  S |n:%  `  : x  <A ??> H  0޽h ? +T3f3f  0 d\P (      3 rA ? 1?"J H   0޽h ? +T3f3f  0 @P(  r  S \M:%  `  :   S `a:&  `<$, 0 : H  0޽h ? +T3f3f___PPT10.+q%9DD' y= @B D' = @BA?%,( < +O%,( < +D-' =%(D' =%(D}' =A@BB BB0B%(E5' =1B B`BPB,54,*3>B ppt_c='`B@BPB<*9D' =1:Bvisible*o3>+#.<*9%(D' =-o6Bdissolve*<3<*9D-' =%(D' =%(D}' =A@BB BB0B%(E5' =1B B`BPB,54,*3>B ppt_c='`B@BPB<*9eD' =1:Bvisible*o3>+#.<*9e%(D' =-o6Bdissolve*<3<*9eD-' =%(D' =%(D}' =A@BB BB0B%(E5' =1B B`BPB,54,*3>B ppt_c='`B@BPB<*eD' =1:Bvisible*o3>+#.<*e%(D' =-o6Bdissolve*<3<*eD-' =%(D' =%(D}' =A@BB BB0B%(E5' =1B B`BPB,54,*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+#.<*%(D' =-o6Bdissolve*<3<*D-' =%(D' =%(D}' =A@BB BB0B%(E5' =1B B`BPB,54,*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+#.<*%(D' =-o6Bdissolve*<3<*D-' =%(D' =%(D}' =A@BB BB0B%(E5' =1B B`BPB,54,*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+#.<*%(D' =-o6Bdissolve*<3<*+8+0+0 +  0 0 X(  r  S <;:%  `  : r  S <:&  ` : RB  s *D> @RB  s *D> RB  s *D>@XB  0D>@@XB   0D>PP^B   6DԔp pH  0޽h ? +T3f3f   0   $(   r   S x :%  `  : r   S 4:&  ` : H   0޽h ? +T3f3fj   0 (  r  S |1:& P  : H  0޽h ? +T3f3f 0 |tp (  X  C    wt  S Mw @  w  The idea of social judgement theory is based primarily on the lens model of cognition developed by Psychologist Egon Brunswik in the 1930s and later refined and used as the framework for Ken Hammond s cognitive continuum approach to examining information use in response to decision and judgement tasks. Briefly, it proposes that: For a given judgement task, such as diagnosing whether a leg ulcer has a venous or arterial aetiology there will be a  correct answer  I.e. it is either venous, arterial or a bit of both; an answer which represents what is termed the ecology. The ecology will comprise a number of pieces of information which will be correlated to this ecology. These pieces of information known as cues can be captured (using regression modelling) and their contribution to the ecology modelled. The contribution is obviously weighted  some pieces of information will contribute more than others. If the ecology with its pieces of information intact is somehow represented and presented to judges  commonly in the form of scenarios - then it is possible (again using regression modelling) to ascertain how much weight the judge assigns to the information cue in the context of the judgement task. Given the information from the separate regression modelling exercises we can ascertain the judges relationship to the ecology: Their accuracy (how good at Dx are they) The sources of accuracy or inaccuracy Variability within judges Variability within the ecology Internal consistency with which judges use information Unused information So, SJT exposes can expose the variability in clinician s judgement policies and the information use that underpins these policies. Examine what this looks like.[slide] >Mv,o_H  0޽h ? ̙33n   0 . & p$ (  $^ $ S    w  $ c $ow @  w . Thus far I have tried to show how social judgement approaches can be used to model clinical information  along the lines of researchers such as Robert Wigton and Priscilla Harries. What I am proposing to use SJT for however is to try and establish the weighting that research evidence has when presented to nurses for use in their clinical judgements and decisions. An evidence based decision  as I am sure I don t need to tell this audience  is one in which due weight is given to research findings, in the context of other sources of information (expertise, resource limitations and patient preference). At the moment we simply do not know how much weight nurses give to research evidence. The literature on information need recognition and seeking (see the excellent synthesis undertaken by donald case) suggests that formal research information consumption as a response to recognised information need is unlikely (in what ever profession you belong to) and my own team s primary research over the last 5 years supports this in the areas of acute and primary care nursing. But nurses get knowledge from sources such as colleagues and their advice appears influential  what SJT will allow us to gain a purchase on is just how influential and what components of the message have most impact. 6K |C,H $ 0޽h ? ̙33? 0 ((  (X ( C    w ( S vw @  w IThe following few slides are from a small piece of pilot work we undertook as part of the development of an on-line judgement policy capture system (the url of which I shall present at the end). At the heart of the exercise was the judgement task of diagnosing hypovolaemic shock in an elderly lady in a critical care environment. We represented the ecology by generating a range of scenarios using a fractional factorial design which produced an orthogonal array of main effects. We used six pieces of clinical information represented at 3 levels of abnormality (normal, equivocal and abnormal). We asked nurses to assess the likelihood (given the information presented) that the woman was in shock and this slide illustrates the variability in response. The nurses undertaking this exercise were 40 newly qualified UK staff nurses having just undertaken a 6 week critical care continuing professional development educational module. For each of the 20 scenarios the mean likelihood and ranges (as well as outliers) reveal the variability in in the estimates of likelihood of shock. [slide],j 7H ( 0޽h ? ̙33B  0 ,(  ,X , C    w , S dw @  w Similarly, when asked to make the dichotomous judgement of whether the patient is in shock or not. We find that - with the exception of scenario 20  clinical disagreement was common. [slide]H , 0޽h ? ̙33> 0 0(  0X 0 C    w 0 S w @  w 6Exposing the variability in the end product of information use is fruitful but of more use to those of us interested in the  black box nature of the relationship between clinical judgement and information use in practice are the data appertaining to the utility or relative contribution each piece of information makes to the clinical judgement. [click] Here we see the relative contribution that each piece of information makes to the sample s judgements of shock. And this tells us that respiratory information is the most important cue for the group as a whole. [click] But if the variation in importance attached to the information is examined at the level of individuals it is clear that significant variation exists  ranging from 8 to 55% in the case of respiritory rate. [click]>et?H 0 0޽h ? ̙33 0 4a(  4X 4 C    w 4 S hw @  w c We can increase the value of the analysis even further by examining the relationship between the level of the information and the judgement. First, we can ascertain the part worth or utility associated with the level at which information is presented. The utility of a piece of information is the effect it has on the likelihood of a Dx of shock. We can see here that information suggesting and abnormal GCS was the most likely to lead to a Dx of shock. [click] Again, however we can see that this group picture masks significant variability in the use of the information at the level of the individual clinician. We see for example that an abnormal GCS score is interpreted by some clinicians in a negative direction (I.e. the more abnormal the less utility!). [click] >OiJH 4 0޽h ? ̙33l  0 ,$8(  8X 8 C    w$ 8 S Dw @  w By looking at the information at different levels for the group we can see that there are clear threshold effects in the ways that the information is utilised. What this means is that by manipulating the presentation of information we may be able to find optimal levels of presentation  and by using dummy variables in the regression model this could be qualitative messages for practice as well as quantitative effect sizes or other data. [click] H 8 0޽h ? ̙33 0 <!(  <X < C    w < S Dw @  w #Talk from slideH < 0޽h ? ̙33z  0 : 2 @ (  @X @ C    w2  @ S Lw @  w  Four main advantages associated with SJT. It recognises the distinction between information and knowledge. Nurses encounter unsynthesised, acontexual, information all the time  but it appears to rarely make a difference to choices. SJT affords the opportunity to present information in its ecological context and so, using the language and settings of participants, we can move the information presented towards the knowledge end of the information knowledge continuum. The ecological validity is retained in ways that are not necessarily a feature of other methods such as think aloud or observation. Sensitivity analysis, nested experimentation and qualitative versus quantitative cue presentation are all options which allow the testing of assumptions about the validity of the method used. The method forces people to make real choices. In this way it is less prone to hindsight and the other biases that impact on self-report literature. Evidence based decision making is heavily value laden and the impact of bias on self report cannot be discounted. Finally, it offers a means of making predictions about level, type and impact of information. These predictions can be validated and used as the platform for the development of decision support mechanisms with a higher degree of salience than might otherwise be expected. [slide],~  H @ 0޽h ? ̙33\  0   D(  DX D C    y D S X$y @  y TThe downside of the method is that truly complex judgements require a large factorial design to ensure representativeness  even with fractional designs. Paul Slovic and others suggest that humans only work with seven cues (+/-) 2. The task is to find those seven cues& And this gives rise to the next disadvantage  the scenario based front end of the research (ie. The bit that most people see) belies the huge amount of effort that has to be undertaken in order to get to that stage: preliminary interviews, trying to get an adequate data set for the ecology, constructing an adequate factorial design for the scenarios, piloting the scenarios and finding efficient and effective ways of presenting them& and all of this for just one side of the model and before you have even captured the judgement policies of clinicians. Quality assuring the data is of course paramount as it really is a case of garbage in, garbage out. 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