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72 The APA Publishing Textbook of Mood Disorders, Second Edition
https://t.me/med1917
TABLE 4–1. Validated functional impairment scales
No. of
Validated scales items Domains
Social and Occupational Functioning
Assessment Scale (SOFAS) and the
related Personal and Social
Performance (PSP) scale
Global Assessment of Functioning
(GAF) scale
Work and Social Adjustment Scale
(WSAS)
Longitudinal Interval Follow-Up
Evaluation Range of Impaired
Functioning Tool (LIFE-RIFT)
Assessment of Impairment
of Functioning/Disability
from the Mini International
Neuropsychiatric Interview
for DSM-5
Life Functioning Questionnaire
(LFQ)
Endicott Work Productivity Scale
(EWPS)
World Health Organization
Disability Assessment Schedule
(WHODAS 2.0)
1 Social, occupational
1 Severity of symptoms and functional
alteration
activities, family life/home responsibilities
5 Work, home management, social and
personal leisure activities, relationships
9 Work, interpersonal relations, general
satisfaction with functioning, recreation
12 Work, social life, leisure ac tivities, family
life/home responsibilities, relationships,
getting along with others, ability to
communicate, self-care ability,
interpersonal aggression/disruption,
financial impairment, physical
impairment, spiritual/religious
impairment, impairment of family by
symptoms
14 Tasks at work/school, tasks in the home,
leisure time with family and friends
25 Work efficiency and productivity
36 Cognition, mobility, self-care, interpersonal
actions, activities, participation in society
international clinical trials. The SDS is very sensitive to change effects and is very sensitive as an efficacy signal detector, often detecting such signals when the other outcome measures fail. It has been used as the key measure for regulatory agency
approvals for improving functional impairment with several medications for mood
disorders. The SDS, which has been translated into more than 80 languages/language
variants, assesses the three most important domains of functioning: work and school,
social life/leisure activities, and family life/home responsibilities. It captures days
lost and days underproductive at work. It accommodates 13 different time frames. It
is very short, simple, and clear, appearing on one page. The SDS can be rated by patient or clinician, although it is much more frequently used as a patient-rated scale. It
uses a unique discretized-analog (Discan) metric that was specifically designed to be
role, parent role, family role
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sensitive in detecting efficacy signals in treatment studies. The Discan metric is anchored numerically (0–10), verbally-descriptively, and visually-spatially to enhance
sensitivity across a population of subjects. Several variants of the scale with addi
tional domains, including a child and adolescent version, are available for specific settings and needs (Sheehan and Sheehan 2008; Sheehan et al. 1996). The SDS has been
expanded to cover 12 domains in an add-on module to the MINI 7.0.2 for DSM-5
(Sheehan 2020).
The five-item Work and Social Adjustment Scale (WSAS) (Mundt et al. 2002) is
very similar to the SDS, and its design was largely based on an earlier SDS. It assesses
five domains and uses a 0–8 scale of response options. It has acceptable internal con
sistency, test-retest reliability, and sensitivity to change.
The nine-item Longitudinal Interval Follow-up Evaluation Range of Impaired
Functioning Tool (LIFE-RIFT; P. Fisher, A.C. Leon, and M.E. Coles, unpublished man
uscript, New York, New York State Psychiatric Institute, 2002) assesses functioning in
the domains of work, interpersonal relations, life satisfaction, and recreation. This
scale has been found to have good reliability and validity (Leon et al. 1999). It has
been used in a longitudinal study of bipolar I disorder with good results (Leon et al.
2000) and has been adapted for children and adolescents (P. Fisher, A.C. Leon, and
M.E. Coles, “The Longitudinal Interval Follow-up Evaluation Range of Impaired
Functioning Tool (LIFE-RIFT) Adapted for Children and Adolescents,” unpublished
manuscript, New York, New York State Psychiatric Institute, 2002).
The 14-item Life Functioning Questionnaire (LFQ) (Altshuler et al. 2002) was designed to assess functioning in four domains: workplace, duties at home, leisure time
with family, and leisure time with friends. It was found to be a reliable, consistent,
and valid assessment of function at work and home in a study of patients with a
mood disorder.
The 25-item Endicott Work Productivity Scale (EWPS) (Endicott and Nee 1997) focuses on work productivity and efficiency.
The 48-item self-rated Social Adjustment Scale (SAS) (Weissman and Bothwell
1976) is the oldest and most detailed of the functioning scales. It was used in many
epidemiology studies and found to be very informative across a wide range of domains. However, because of its length, it is less suitable for routine clinical use or as
an outcome measure in treatment studies. However, it remains the mother of most
functional impairment scales.
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Assessment Instruments for Suicidal Ideation
and Behavior
More attention has been paid to the development of assessment instruments for suicidal ideation and behavior in the past decade than to scale development in any other
area of mood disorders. Meyer et al. (2010) reviewed the status of the 29 most important assessment scales following a large international consensus conference on suicidality and suicide risk in 2009. Nine of the 29 scales were considered to have achieved
a higher level of development than the others. Several of the more important scales in
that review have undergone significant updates and revisions since then. The standards against which they were judged were the categories in the 2010 Draft Guidance
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of the U.S. Food and Drug Administration (2010). This guidance underwent significant revisions in 2012 (U.S. Food and Drug Administration, Center for Drug Evaluation and Research 2012) and is undergoing further revision at the time of this writing
in 2021.
Some of these scales were developed for adults, others for adolescents, and still
others for children. Some are self-report measures, others are clinician administered,
and some accommodate both routes of administration. Some focus on the assessment
of suicidal ideation, others evaluate suicidal behaviors or self-injury, and others assess a wider range of suicidality phenomena across the spectrum of suicidality.
To be acceptable for use in a clinical trial or as an outcome measure of safety or efficacy, a suicidality assessment scale must map closely or precisely to the categories
outlined in the 2012 FDA draft guidance document (U.S. Food and Drug Administra
tion, Center for Drug Evaluation and Research 2012). In 2021, the following five scales
were deemed to meet this standard: the Columbia–Suicide Severity Rating Scale (C
SSRS) (Posner et al. 2011), the Suicide Ideation and Behavior Assessment Tool (SIBAT)
(Alphs et al. 2017), the Sheehan–Suicidality Tracking Scale (S-STS) (Sheehan et al.
2014b), the Sheehan–Suicidality Tracking Scale Clinically Meaningful Change Measure (S-STS CMCM) version (Sheehan et al. 2014a), and the InterSePT Scale for Suicidal Thinking (ISST Plus) (Lindenmayer et al. 2003). All five scales can be clinician
rated. The S-STS and the S-STS CMCM can also be patient rated. The SIBAT has some
patient-rated components. The S-STS is the only suicidality assessment scale that has
been linguistically validated for children and adolescents (Amado et al. 2014). It is
available in three versions for three different age groups.
The C-SSRS was originally developed as a safety assessment questionnaire with binary (yes/no) response options. It is therefore a questionnaire rather than a dimensional rating scale and consequently does not lend itself to use as a sensitive treatment
outcome measure capable of detecting an efficacy signal. Although it was a welcome
improvement at the time over preexisting suicidality assessment methods in assessing treatment-emergent suicidality in clinical trials, it has many well-documented
limitations (Giddens and Sheehan 2014b; Giddens et al. 2014).
The Beck Scale for Suicidal Ideation (BSI) (Beck et al. 1979) has also been used as
an efficacy outcome measure. The BSI assesses a narrower range of the spectrum of
suicidality than many of the other suicidality scales. One result is that it does not detect some important treatment-emergent suicidality phenomena. Overall, the BSI has
been the least sensitive assessment in detecting treatment effects and in discriminat
ing between active treatment and placebo for suicidality. Both item 10 on the MADRS
and the S-STS have been more sensitive in detecting these efficacy signals in doubleblind, placebo-controlled trials (Canuso et al. 2018; Khan et al. 2011). Suicidality assessment measures continue to evolve rapidly (Chappell et al. 2014).
There is much more to suicidality than suicidal ideation and behavior. Suicidality
also includes suicidal impulses, suicidal hallucinations, delusional suicidality, and
suicidal dreams. A good suicidality assessment scale should ask about the full range
of suicidality phenomena. It should be sensitive in simultaneously detecting treatment efficacy and treatment-emergent safety effects (e.g., increased suicidality) and
in detecting differences between drug and placebo. Such sensitivity is ethically necessary to identify efficacy and safety signals in as small a sample size as possible,
while exposing the fewest possible number of patients to risk in clinical trials.
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Using one or two suicidality items from a depression scale (e.g., item 3 of the HAMD, item 10 of the MADRS, or item 9 on the PHQ-9) to screen for suicidality is a mistake.
If you want to screen for suicidality, screen for suicidality directly rather than screen
ing for one of its proxies, and assess the full range of suicidality phenomena in the process (Preti et al. 2013). This matters because the progression of any one suicidality
phenomenon over time is not related to any other in a precise linear fashion (Sheehan
and Giddens 2015). Their relationship to each other is almost always polynomial to the
fourth order or higher. You cannot accurately predict the presence or magnitude of one
by the presence or absence of another (Sheehan and Giddens 2015). As a result, you
cannot rely on any one phenomenon to tell you whether any other suicidality phenom
enon is present or absent or to what extent within a time frame. Time spent per day in
suicidal ideation/behavior and the total S-STS score may be more accurate and sensi
tive measures of global suicidality than a global severity of suicidality measure, especially at the more serious end of the spectrum (Giddens and Sheehan 2014a, 2014c).
When using a suicidality assessment scale, you should adopt the mindset that you
are assessing the presence or absence of suicidal phenomena within a recent past time
frame while at the same time avoiding the temptation to make predictions about future
suicidality. You cannot accurately predict suicidality at the individual level within a
narrow time frame. The reason is that the progression of suicidality over time within
a single individual is nonlinear when mathematically modeled (Sheehan and Giddens
2015). You do not use a schizophrenia scale to predict if and when someone is going
to get a future hallucination or delusion, and you do not use a panic disorder scale to
predict if or when someone is going to have another panic attack. Instead, you mea
sure what has already occurred and you make your management decisions accordingly. Assessment of suicidality calls for the same approach as assessment of other
areas of mental health. My recommendation is that you get copies of the various
scales, try them out with patients, get patient feedback on them, and see whether they
meet your needs and address your clinical and research questions.
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Global Severity and Improvement Scales
The Clinical Global Impression—Severity (CGI-S) and Clinical Global Impression—
Improvement (CGI-I) scales have been widely used to assess illness severity and
global improvement or change. Both are seven-point scales. The CGI-S rates severity
from 1 (normal) through 7 (among the most severely ill patients) (Guy 1976). On the
CGI-I, there are three points of deterioration and three points of improvement, with a
point denoting no change in between (Guy 1976). The CGI-I met the needs for an im
provement scale when it was first developed and used. However, with increasing
numbers of treatments and an increasing need to detect smaller differences in efficacy
between multiple treatments in the same study, it became necessary to develop more
sensitive global improvement scales. To meet this need, the Sheehan Clinical Global
Improvement (S-CGI) scale and the Sheehan Patient Global Improvement (S-PGI)
scale were developed. These measures of improvement are 21-point scales using a Discan metric for the response options. There are 10 points of improvement (1–10) and 10
points of deterioration (–1 to –10), with a zero (0) point in the middle, reflecting the
baseline starting point score in the study or no change at any visit (Sheehan et al. 1988).
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Comparison of Patient-Rated and Clinician-Rated
Scales of Mood Disorders
Self-rated scales take less clinician time, but are they comparable to clinician-rated
scales? Early comparisons in the adult depression scale literature (Carroll et al. 1973;
Prusoff et al. 1972) found only moderate correlation between self-rated scales and cli
nician-rated scales (r=0.11–0.63 and r=0.42, respectively); this may be because different scales were used in the comparisons. The Carroll (Depression) Rating Scale (CRS)
is a scale specifically designed to correspond with the 17-item Hamilton Depression
Rating Scale (HAM-D). Even though they are very closely related instruments, a com
parison between the two yielded a correlation that was still far from ideal, although
it was higher (r=0.67) than that usually found between self-rated and clinician-rated
scales. The conclusion was that self-rated depression scales are less reliable than cli
nician-rated depression scales (Carroll et al. 1981). That conclusion is open to debate.
Biases (sometimes self-serving) are inherent in clinician-rated scales. Clinicians using
a clinician scale sometimes have their own rating prejudices and preconceived ideas
that are at variance with other raters. Similarly, patients using a patient-rated scale
may not be able to accurately rate the severity of their symptoms in relation to how
other patients rate the same symptoms. In the absence of information about how
other people experience depression, one patient may overvalue or underestimate the
severity of one or another symptom in the depressive cluster, compared with another
patient’s ratings. However, both clinician and patient perspectives have merits and
limitations.
Experience has taught me that as patients became more familiar over time with
completing these scales and reviewing and discussing them with their clinicians,
their scores grow closer to the clinicians’ ratings. I have more confidence in patient
ratings than many other clinicians do. Clinicians should use them more frequently
and should spend more time up front training patients in the proper and accurate
completion of rating scales.
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Interpretation of Scale Scores
Use and Misuse of Factor Scores
Factor scores have been extracted from existing scales based on factor analytic studies.
For example, somatic anxiety/somatization, psychic anxiety, pure depression, and an
orexia factors have been identified for the HAM-D. However, these factors are not stable across different samples in different settings. Identifying items belonging to these
named factors and then studying the effect of treatments on them in subsequent studies is a misleading process. For example, in a very large dataset used to investigate
anxiety in clinical trials with depressed patients, we (Goldberger et al. 2011) found
that the “use of Cleary and Guy’s anxiety/somatisation factor from the HAMD
unstable across studies and is not an appropriate routine measure of anxiety in depressed patients” (p. 48). We suggested that “in clinical trials, other scales specifically
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designed and validated for anxiety assessment should be used. An alternative strategy is to assess the factorial structure of the HAMD
and use for outcome assessment the factors identified by this analysis [within each
study sample]. Such ‘tailored’ factor analysis would retrospectively allow the identi
fication of the set of items appropriate for each specific study sample” (p. 48).
scale for each studied sample
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Response and Remission Cutoffs
Response for each individual on any scale is usually defined as a 50% or greater reduction from the baseline score. Remission for each individual on any scale is usually defined as a 70% or greater reduction from the baseline score. Remission is typically
anchored to a reduction of 2 standard deviations from the baseline mean. Patients are
said to be in remission when their scores reflect their own expectations of a good out
come, even if the score is less than perfect. Remission is also said to reflect an experienced clinician’s expectations of a good treatment result, even if the outcome is not
yet perfect. In mood disorders there is remarkable confluence and consistency across
all four of these anchors of remission. These four anchors of remission line up with
remission scores of 7 or lower on the HAM-D17 and 10 or lower on the MADRS.
In recent years, some pharmaceutical companies and researchers have used a score
of 12 or lower on the MADRS as a remission cutoff score. Clinicians should beware of
cutoffs like this. Such reports can be self-serving, instigated without justification by
marketing divisions so that a larger percentage of patients can be declared in remission
in the interest of making an investigational product appear more effective than it really
is. Instead, such reports have the opposite effect by diminishing the companies’ image
in the eyes of the reader. Inflating results in this way has negative consequences. These
include reducing interest in investing in new treatments by suggesting 1) that existing
treatments are better than they are, 2) that we do not have unmet treatment needs, and
3) that we do not have a long way to go in improving the efficacy of current treatments.
No clinicians should participate in these charades.
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Challenges in Developing Scales
Based on my experience in developing several scales, at least $2 million is spent per
scale before it is widely adopted, translated into many languages, and used and accepted internationally. If a scale is good, it takes about 7 years of work before it gains
traction in the field and 15 years before its use grows exponentially. The hurdles in
volve psychometric testing, validation and reliability studies, extensive patient and
expert opinion input, cognitive debriefing, overcoming hurdles and expectations of
regulatory agencies, and linguistic validation and cognitive debriefing for all translations into other languages. That is why there are so few very good and widely used
scales across the entire field of mental health. Many developers give up long before
they reach the goal, because they get discouraged at the time and/or expense involved. Scale development requires a thick skin and endless persistence. The results
are rewarding, however. What is important is to get started and to stick with it. The
biggest progress is made in the first 2–3 years, if the scale developer stays focused on
designing a scale that is “fit for purpose.”
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Advice on Use of Scales in Clinical Practice
You as a clinician may hope to administer scales and structured diagnostic interviews
during clinic visits, but you will not have enough time. If you expect success, then
have the patient complete a self-report scale before the start of the visit. Schedule pa
tients early so they have time to complete the battery of scales before they see you.
Arrange for front office staff to hand these scales to patients on a clipboard to com
plete in the waiting room before their appointments. Alternatively, email the patients
the battery of scales individualized for them to complete on the day before their ap
pointments. Ask patients to bring their completed scales to the visit. Let them know
they must complete the scales before seeing the clinician. This practice will signifi
cantly improve the success and adherence rate. At the start of the visit, quickly review
the completed scales, thank patients for completing them, and tell them how helpful
this information is to you in your decision making. Use the scale responses as a basis
for more in-depth probing or follow-up questions.
Ecological Momentary Assessment
Ecological momentary assessment (EMA) involves repeated sampling of an individual’s symptoms, behaviors, functioning, and cognitions in real time. This can be done
using mobile devices or microprocessors to sample subjects in their real-world and
natural environment for a few moments during their day, often using random timing.
It minimizes recall bias and maximizes ecological validity—that is in the individuals’
ability to relate to themselves, to other people, and to their environment.
EMA most frequently utilizes mobile phones or wearable devices like heart monitors, electronic wrist bands, or rings with built-in technology. The information is often
linked to a mobile phone app, which in turn synchronizes data to remote sites, where
the data are gathered across large study samples. The use of EMA is rising rapidly.
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Advice on Use of Scales in Research Settings
Number of Scales
A common problem in a research study is using too many scales. As you do more research across time, you will learn to minimize the number of scales you will use. In
your early-career studies, try to resist participating in “fishing expedition studies” with
a wide range of scales. Stay focused on addressing your narrow research questions.
Statistical Analysis
When you plan a study involving rating scales and structured diagnostic interviews,
you need to clearly state your plans for how to collect and analyze your scale data.
Your study design and analysis should be precisely aligned to address your research
question. Your hypotheses should be clearly and explicitly stated a priori. If you have
several hypotheses, they should be prioritized. All this is best done in consultation
with an experienced statistician. Presenting your dataset for the first time to statisticians after the study is completed and then expecting them to find something of in-
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terest by fishing around in your dataset elicits sighs of despair and is an all-toofrequent occurrence. You cannot rescue by analysis what you have already damaged
in your research design. There are many fine books on this topic. My favorite is Krath
wohl’s superb classic on preparing a research proposal (Krathwohl and Smith 2005).
Reporting Results
It is important for clinical researchers to follow the Consolidated Standards of Reporting Trials (CONSORT) statement proposed by the Journal of the American Medical Asso-
ciation when reporting results of studies that use rating scales as endpoints (American
Educational Research Association 2014). The figures and tables in a research report
should clearly identify the specific statistical test and the dataset—whether it is a com
pleter analysis, an observed case, a last observation carried forward, or a modeled
dataset (as in MMRM [Mallinckrodt et al. 2001] or ET Rank analysis [Entsuah 1996]).
There are different levels of scientific confidence associated with each (Begg et al.
1996). In addition to the sources cited above, readers seeking further guidance on
these different datasets are referred to Elobeid et al. (2009).
Validation
Validation is a fundamental and necessary step in scale development. It refers to the
evidence in support of the interpretation of the scale scores for the proposed use and
the extent to which it is “fit for purpose.” However, there can be an overemphasis on
validating any new scale against some existing “gold standard” scale. The problem is
that there may not be gold in any so-called gold standard scale. When two scales are
compared or “validated against” each other in this manner, it is like studying the correlation or agreement between two clouds floating across the sky. Neither cloud is anchored to anything on the ground. If the so-called gold standard is itself flawed, then
showing a high agreement or correlation with it is not necessarily a positive attribute.
When I am contacted about one of my scales or structured interviews and the first
question is “Has your scale been validated?” I know I am dealing with someone who
is not sophisticated in scale selection or evaluation. Many scales in mental health are
flawed in one way or another and need to be improved. In the validation testing of a
new scale, being able to demonstrate discrepancies or nonagreement with a preexist
ing standard may be a positive attribute rather than a negative one. Indeed, it may
indicate that the new scale has improved on an older reference scale in an area of
known weakness in the earlier scale. When there is no standard scale in a new area of
investigation, it is difficult to validate a new scale in this way. Standards for Educational
and Psychological Testing (American Educational Research Association 2014) is manda
tory reading for everyone involved in scale development and evaluation.
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Reliability
In contrast to the tendency to overemphasize validation in scale development, there
is an underemphasis, in my opinion, on the importance of reliability testing. Interrater, intrarater, and test-retest reliability are all critically important properties in the
use of scales in mental health research. Good reliability lends confidence to administering a scale in a large multisite study involving many raters over time. For a detailed
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discussion of reliability, readers are referred to the Standards for Educational and Psychological Testing (American Educational Research Association 2014).
Linguistic Validation
Linguistic validation is also too often neglected, misunderstood, and undervalued. It
needs to be much more highly valued and much more frequently included in the
planning of scale development and international study planning. Linguistic valida
tion is very expensive and time consuming, but it is a necessary step to ensure good
interrater reliability across collaborating study sites and international investigators.
This process is well delineated in publications by the International Society for Phar
macoeconomics and Outcomes Research (ISPOR) Task Force for Translation and Cultural Adaptation (Wild et al. 2005, 2009). The process involves a forward translation
of the source scale by one research team. A second team, blind to the original lan
guage’s source scale, then back-translates the forward translation into the original
source language. The back-translation is then compared with the original source lan
guage scale by a third team, which includes expert bilingual consultants who have expertise in the mental health specialties in collaboration with input and consultation
from a consultant at a translation and linguistic validation service, such as Mapi Re
search Trust in Lyon, France. Final reconciliation between all involved, including the
scale author or developer, completes the process. Every step by all involved in every
stage of the process is documented in a spreadsheet. For example, a typical spreadsheet for the translation and linguistic validation of the MINI into one language is six
columns wide and 10,000–15,000 rows deep. When the process is complete, the lin
guistic validation service provides certification of the process. This documentation is
then available for audit and review by regulatory agencies, research organizations,
and sponsors with oversight responsibilities for the study.
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Importance of Sensitivity in Detecting Change Over Time
and in Identifying Differences Between Effective
Medications and Placebo
Efficacy signal detection, or the ability to detect a statistically significant difference
between an active medication and placebo in a clinical trial, is a highly valued and
essential property of any scale used in monitoring treatment outcomes. Scales that are
consistently successful in this regard are more widely used, are more highly regarded,
and survive longer than scales that do not have this property. When scales are found
to lack this property, their use thereafter goes into decline.
Virtual Administration of Scales and Structured
Diagnostic Interviews During Pandemics
Such as COVID-19
During the COVID-19 pandemic, I regularly encountered problems while consulting
on the use of scales and structured interviews during virtual administration. These
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issues merit further attention, investigation, and study to restore confidence in the
integrity of the process. The following are some concerns regarding and recommen
dations for improving the issues that became apparent in virtual administration of
scales and structured interviews:
1. The strengths, weaknesses, limitations, sensitivity, and specificity of administration
of scales and structured interviews conducted during virtual administration versus
live face-to-face interviews need to be carefully studied. Researchers need to inves
tigate telephone interviewing; virtual interviewing on Zoom, Doximity, Skype, and
WhatsApp; and the different technologies on which apps are used, such as mobile
devices (phones) or desktops, laptops, or tablet computers. The use of the various
platforms may result in alterations in sensitivity, specificity, and efficacy and safety
signal detection. Screen sharing on several of these platforms may come very close
to in-person interviews in sensitivity and specificity. There may even be some advantages in signal detection during virtual administration. However, there may be
a loss of sensitivity and efficacy and safety signal detection while using information
technology (IT) versions or during virtual administration of rating scales.
2. Because these virtual modes of administration are likely to become more common
in the future, we need more data to justify the pooling of data captured in these
diverse ways across sites and over time in a single study in order to have confidence in the integrity of the process.
3. During the pandemic, restrictions on security concerns in the collection of health
care information were relaxed in the interest of safety and to ensure proper social
distancing. Policies to ensure proper compliance with the Health Insurance Porta
bility and Accountability Act of 1996 (HIPAA) and the General Data Protection
Regulation (GDPR) (Politou et al. 2018; see also Pew Research Center 2014) need
to be reviewed and revised for future research studies.
4. The IT version of a scale or interview on a software platform may not be exactly
the same as a paper or PDF version. The layout of a scale on a page can affect both
response and error rates.
5. In reviewing software implementations of scales and structured interviews, I regularly encountered software coding errors by IT developers and have learned that
the scale authors are frequently not involved or consulted in the certification of the
IT versions. Closer collaboration is necessary.
6. The use of scales and structured interviews in data entry/capture software has too
frequently fallen short of ideal implementation. IT developers and their sponsors
have frequently refused to update or to correct updated versions of the scales and
structured interviews and have failed to update these to the correct and current
linguistically validated translations. This is a particular problem in ensuring consistency with updates to DSM.
7. Training of raters in the use of remote data entry software is incomplete when versions of scales suitable for virtual administration are used. During a pandemic, it
is not possible to have safe in-person investigator meetings to ensure good reliabil
ity and consistent implementation across sites.
8. Missing data, double entries, and input errors can be avoided by careful software
design on the platform used for data capture and entry. Many data capture software programs are not designed to minimize these problems.
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