KUMAR-GUPTA ENVISAGED DIVERSITY REALIGNMENT SCORE EXPLAINED
Channel: SoskinGupta
Date: 2020-11-24

How to appropriate diversity among Graduate Medical Education (GME) interviewees according to diversity among Electronic Residency Application Service (ERAS) applicants before calling them for interviews; Correspondingly, how to appropriate diversity among National Resident Matching Program (NRMP) Match according to diversity among GME interviewees before ranking them in THE MATCH


DETAILED EXPLANATION HOW DIVERSITY REALIGNMENT SCORE HAS BEEN ENVISAGED


VIDEO DOES NOT HAVE REAL PERSONS' DATA BUT SIMULATED DATA HAS BEEN CREATED


ni: number of interviewees (input) being evaluated (in our example ni=100)


no: number of residents (output) to be selected finally from among ni (in our example no=10)


nx: number of non-modifiable characteristics' categories (as sex, birth year, race, graduation year in our example, so nx=4)


f1,f2,f3,......,fa: frequency of individual characteristic in the category (as 57 males in our example, so fa for male (fmale)=57)


py=fa/ni: probability of individual characteristic in the category (in our example py for male (pmale)=0.57)


Sz=[no]-[no*py]: individual's characteristic based individual category's score (in our example Sz for sex if individual is male (Ssex(male))=4.3)


C=S1+S2+S3.......+Sz: individual's cumulative score for all categories on 0-[no*nx] scale (in our example's first individual in first row, C=Ssex+Sbirthyear+Srace+Sgraduationyear=29.6 on 0-40 scale)


R25=[25*C]/[no*nx]: individual's cumulative score realigned to 0-25 scale (in our example's first individual in first row, R25=18.5 on 0-25 scale)


R100=[100*C]/[no*nx]: individual's cumulative score realigned to 0-100 scale (in our example's first individual in first row, R100=74 on 0-100 scale)


Thereafter, R25 (or R100) based on non-modifiable characteristics' categories can be added to 0-25 (or 0-100) scaled cumulative scores given by the panel of interviewers to the interviewees per their assessment of interviewees during interviews


Similarly, say 4000 applications can be scored to finalize 100 interviewees following the above-model to decipher R25/R100 scores so as to add to 0-25/0-100 scaled scores given to the applications by the reviewers of applications


Essentially, this way there can be 50:50 weightage to the non-modifiable characteristics of those being judged with modifiable characteristics per the judges so as to attempt sustenance of the diversity in the pool during the various stages of recruitment with R25/R100 scores countering the inadvertent explicit/implicit bias of the judges when they are judging those to be judged on 0-25/0-100 scaled scores during their interviewees’ interviews or their applications’ reviews


Diversity among output may be maintained only as good as diversity among input and hence the correction of diversity to be as good as diversity among general population may not be warranted considering that which general population can be considered as the representative for any process may always be ambiguous at best


Diversity realignment scores (R25/R100) should not be disclosed to the judges judging interviewees/applications to avoid preemptive awareness of R25/R100 scores about interviewees/applications making judges to inadvertently accentuate/attenuate their own 0-25/0-100 scaled scores while allotting them to interviewees/applications


Only time may tell whether R25/R100 scores will just overcome the bias of overabundance favoring the majority based on the diversity among input or whether R25/R100 scores will inadvertently over-correct the bias against under-abundant minority thus completely reversing the diversity among output as compared to the diversity among input especially when those in minority in the input may anyway be getting preferred by judges who may not be blinded to interviewees’/applications’ diverse characteristics