Therapists are bearing the brunt of our mental health crisis, and burning out. Over-burdened by client load, while maintaining boundaries and staying on top of admin duties, many are exploring ways to optimise their workflows. 

When it comes to tracking client progress, outcome measures offer an unparalleled way to measure complex and subjective perspectives of how their clients are progressing, but they aren’t without flaws!

Presenting issues can be complex, and the best method of measurement depends on your clients and their circumstances. With an ever growing library of outcome measures, Bravely Connect is your go-to tool!

As a clinician, monitoring your progress with proper, measured data gives an objective way to judge progress and adjust treatment approach. However, quantifying the diverse range of human emotions and experiences presents significant challenges.

Measuring outcomes requires a measured approach

When considering the appropriateness of outcome measures for every client, more than half of clinicians in one study had concerns for certain patient populations (Garland et al., 2003). 

These concerns are not unprecedented, especially when factoring in demographic factors like gender. The healthcare field has long struggled to ensure equitable representation of women in research with many early phase pharmaceutical studies not including women at all (Ortona et al., 2016). 

Unfortunately, the mental health field too often suffers from similar biases, even when developing outcome measures intended to objectively assess certain phenomena across cultures and genders. Women may respond differently to a “validated” assessment than men. Until recently, transgender and nonbinary people were rarely considered in validation studies at all.

The origins of outcome measure biases

Some validated outcome measures centered on objective assessment become more ambiguous when assigned across genders. For example, the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C), a self-report measure used to screen for problematic drinking, was first validated using a sample of all men; women were excluded from this validation study for statistical reasons as there were not enough available in the potential sample pool to get usable data (Bush et al., 1998). 

Subsequent studies of the AUDIT-C including women have found that men and women differ in when a score indicates problematic drinking; with men, a score of 4 or higher means a potential drinking problem, while women need a score of 3 or higher.

Another outcome measure, the Autism Spectrum Quotient (AQ), also performs differently among genders to women’s disadvantage. Many of the AQ items assess struggles with socializing, a trait of autism spectrum conditions that appears much more frequently in men than women (Belcher et al., 2023), leading to the AQ being less sensitive in detecting autism in women than in men. 

Some research theorises that these differences in gender presentation may be in part due to women and girls being socialised to better “mask” any social deficits (Krahn & Fenton, 2012). 

Other research notes that, once again, the study in which this measure was developed used an overwhelmingly male sample (Belcher et al., 2023), potentially leading to a measure better attuned for assessing autism as it presents in men and boys.

Tips for using outcome measures & reducing bias

While we believe in the power of measurement based care, given the gender (and many other) biases that can be inherent in these measurement tools, we highlight the importance of being familiar with the tools you use. We have some tips for better outcome measure use:

• Ask your supervisor or a peer you trust about what measures they’d suggest for your client’s specific case

• Consider the cadence of testing (with Bravely Connect, clients can complete measures more frequently than appointments) 

• Get familiar with the measure, such as it’s questions

That last one is often a barrier that can be tough. Fortunately, we have a library of in depth, referenced summaries with many popular measures, such as the DASS, & K10, with information on scoring, use cases and demographic concerns. Also, we highly recommend contextualising results from any outcome measure in the larger context of a client’s life. 

We as human beings contain multitudes, and outcome measures offer glimpses of who we are and how we’re doing. If those glimpses are into areas that you’re targeting during treatment, then fantastic! Just remember that they’re part of a much larger, more complex picture.