If you torture the data long enough, it will confess.
Ronald H. Coase
By Dr. Bob Uttl
Dr. John Braxton Suffield criticized Dr. Mary Westcott’s use of simple, age-based discrimination methods and argued for use of the advanced discrimination methods in fitness for duty assessments that incorporate not only employee’s age but other demographic factors such as sex, race/ethnicity (skin color), and education (see College of Alberta Psychologists approves discrimination in employment..). Accordingly, Dr. Suffield elected to utilize the Pearson Advanced Clinical Solutions (ACS) software package for the Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV; Wechsler, 2008) to evaluate Ms. T’s fitness for duty.
The Pearson ACS is a psychometric tool that mechanically bakes identity adjustments directly into its software algorithms. When a practitioner plugs an examinee’s raw scores into the system, the software applies demographic regression weights. Consequently, the software spits out entirely different clinical descriptors and diagnostic labels (e.g., “Impaired” vs. “Average”) for the exact same raw performance and absolute cognitive ability, solely because of the examinee’s skin color, age, sex, or education.
Because of this built-in design, the deployment of Pearson ACS demographic norms in forensic Fitness for Duty Assessments is inherently discriminatory.
If a school district, for example, the School District No. 5 Southeast Kootenay, requires elementary school teachers to possess a baseline level of cognitive functioning—a specific minimum threshold of attentional capacity or cognitive processing—to safely manage a classroom and perform their teaching duties, that benchmark must be absolute. It must be uniform across all ages, sexes, races, and backgrounds. By using Pearson ACS demographic corrections, Dr. Suffield effectively asserts an unlawful double standard: an identical raw test performance is acceptable for a teacher of one skin color or age, but constitutes a failure and grounds for termination for a teacher of another.
This proves Dr. Suffield was not measuring objective workplace competence; he was running identity-based statistical projections that violate the core of Canadian human rights law. Whether an examinee was branded with the professional stigma of being “Impaired” was dictated in part by the demographic boxes checked on a software screen.
Shifting Descriptors: Turning “Normal” Into “Impaired”
To understand these methods, one must look at how the Pearson ACS fundamentally alters standard psychometric boundaries. An examiner can shift an examinee from unimpaired to impaired merely by switching from standard manuals to the ACS Pearson classification rubric:
- Standard WAIS-IV (2008): Classifies composite scores into traditional intellectual tiers: Very Superior, Superior, High Average, Average, Low Average, Borderline, and Extremely Low.
- Pearson ACS: Replaces these with a clinical impairment paradigm: Above Average, Average, Low Average, Mild Impairment, Mild to Moderate Impairment, Moderate Impairment, Moderate to Severe Impairment, and Severe Impairment.
Under this framework, the Pearson ACS “Low Average” label aggressively encroaches on standard “Average” ranges. More critically, the ACS “Mild Impairment” label pathologizes the standard “Low Average” and “Borderline” ranges. By design, the Pearson ACS classification system labels the bottom 16% of all healthy individuals in any demographic slice as “Mildly” or more impaired.
Under Oath: The Cross-Examination of Dr. Suffield
Here is Dr. John Braxton Suffield, in his own words under oath, admitting that the diagnostic labels he applied to Ms. T—such as “Mildly Impaired,” “Low Average,” or “Average”—depend entirely on skin color and demographic variables:
QUESTION: And here you have the different descriptive labels, than commonly used with WAIS-IV. And these descriptive labels have things like average, low average, mild impairment, mild to moderate impairment, etc., correct?
DR. SUFFIELD: Correct.
QUESTION: And you used these labels [Pearson ACS labels] in your report, correct?
DR. SUFFIELD: I did, for the [Pearson] ACS-derived scores.
QUESTION: Very good. And so now, if you change. if you take the scores which you inputted into Ms. T’s. if you take Ms. T’s scores which you inputted into ACS, and instead of clicking that she was Caucasian, and you click that she was Black, the labels would change, correct?
DR. SUFFIELD: They would change, just as. They would change, just as if I had clicked that she had a Grade 8 education, instead of having 16 or more years of education.
QUESTION: Exactly. And so, whether the person is, according to these labels, mildly impaired, or low average, or average, depends on their sex, race, ethnicity, and education, correct?
DR. SUFFIELD: Correct. The purpose of the ACS demographic correction is to take these demographic variables into account.
Dr. John Braxton Suffield, Cross-Examination, June 18, 2026
Dr. John Braxton Suffield openly conceded that if he had toggled the “Black” box instead of the “Caucasian” box to describe Ms. T’s background, her diagnostic profile would instantly shift to “average” or “above average.”
QUESTION: …. My question is very simple. When you entered Ms. T’s scores into the [Pearson] ACS and you printed whatever the ACS prints, if you clicked that she was Black instead of Caucasian, the labels would change and she would suddenly be average or above average, correct?
DR. SUFFIELD: I believe that’s true. I didn’t do that because she isn’t Caucasian. [Note: Ms. T is Caucasian]
QUESTION: Okay. Would you agree with me that Ms. T’s elementary teacher peers are not only White women with 17 years education, but also Black, Asian, Indigenous teachers, teachers with 16 years of education, 17 years of education or even Master’s degrees or even Ph.D. degrees?
DR. SUFFIELD: That probably is the case.
Dr. John Braxton Suffield, Cross-Examination, June 18, 2026
The Deficient Standard
The raw mathematical reality of Dr. John Braxton Suffield‘s methodology is clear: An identical raw performance yields entirely different diagnostic labels based entirely on demographic factors.
This standard is explicitly not uniform and discriminatory by design. A Caucasian teacher can be labeled “impaired” and removed from her livelihood, while a Black colleague with the exact same raw ability is labeled “average” and deemed fully fit to remain in the classroom. The exact same moving baseline applies across age brackets, sex brackets, and educational achievements.
What makes this clinical choice truly astonishing is that the authors of the Pearson ACS themselves explicitly include a clear warning against using the tool for this exact purpose:
“Demographic adjustments should not be used to determine functional capacity in the general population, job, or education settings…”
Holdnack, J.A., & Weiss, L. G. (2013)
Furthermore, the Pearson ACS package should never have been deployed in this assessment for another fundamental reason: it was developed and standardized strictly using a US population sample. The racial, ethnic, sex, and educational stratification embedded within the US software algorithms cannot simply be assumed to be valid or reliable for Canadian citizens. By importing American demographic corrections wholesale into a Canadian workplace dispute, Dr. Suffield subjected a Canadian educator to foreign, non-representative, and patently invalid metrics.
This methodological overreach directly violates the software’s own operational boundaries. The Pearson ACS software printed the following explicit warning directly on the Demographically Adjusted Score Report generated by Dr. Suffield:
Demographic corrections based on age, education, sex, and four categories of race/ethnicity (White, African American, Hispanic, Asian). Demographically Adjusted Norms have been validated on native English speakers, receiving most or all of their education in The United States of America. Demographic adjustments may not accurately represent performance of individuals for whom English is a second language or were educated outside of The United States of America.
Pearson ACS Demographically Adjusted Score Report (Pearson, 2009)
By ignoring the publisher’s clear counter-indication, Dr. John Braxton Suffield utilized a tool outside of its validated scope, undermining the psychometric integrity and legal defensibility of the entire assessment.
Despite the foreign origin norms and the explicit exclusionary warning from the test developers, Dr. John Braxton Suffield’s methods were fully approved by regulatory overseers at the College of Alberta Psychologists. This includes Dr. Troy Janzen (Deputy Registrar and Complaints Director) and the College Complaints’ Review Committee (CRC), chaired by Dr. Lorraine Breault, and with Dr. Ali Al-Asadi as a professional member of the CRC.
Apparently, it occurred to absolutely no one in the regulatory chain that applying the Pearson ACS foreign demographically adjusted norms to workplace competency evaluations mathematically forces a differential, identity-based minimum discriminatory standard.
References
Holdnack, J. A., & Weiss, L. G. (2013). Demographic Adjustments to WAIS-IV/WMS-IV Norms. In Holdnack, J. A., Drozdick, L., Weiss, L. G., & Iverson, G. L. (Eds.). WAIS-IV, WMS-IV, and ACS: Advanced Clinical Interpretation. San Antonio Texas: Pearson.