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Computation of Effect Sizes

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Abstract

Statistical significance specifies, if a result may not be the cause of random variations within the data. But not every significant result refers to an effect with a high impact, resp. it may even describe a phenomenon that is not really perceivable in everyday life. Statistical significance mainly depends on the sample size, the quality of the data and the power of the statistical procedures. If large data sets are at hand, as it is often the case f. e. in epidemiological studies or in large scale assessments, very small effects may reach statistical significance. In order to describe, if effects have a relevant magnitude, effect sizes are used to describe the strength of a phenomenon. The most popular effect size measure surely is Cohen's d (Cohen, 1988), but there are many more. On https://http://www.psychometrica.de/effect_size.html , you will find online calculators for Cohen's d, Glass' Delta, Hedges' g, Odds Ratio, Eta Square, calculation of effects from dependent and independent t-tests, ANOVAs and other repeated measure designs, non-parametric effect sizes (Kruskal Wallice, Number Needed to Treat, Common Language Effect Size), conversion tools and tables for interpretation. The code for computing these measures is avaliable as Javascript in the header of the source code of webpage.
Computation of Effect Sizes
Statistical significance specifies, if a result may not be the cause of random
variations within the data. But not every significant result refers to an effect
with a high impact, resp. it may even describe a phenomenon that is not really
perceivable in everyday life. Statistical significance mainly depends on the
sample size, the quality of the data and the power of the statistical procedures.
If large data sets are at hand, as it is often the case f. e. in epidemiological
studies or in large scale assessments, very small effects may reach statistical
significance. In order to describe, if effects have a relevant magnitude, effect
sizes are used to describe the strength of a phenomenon. The most popular
effect size measure surely is Cohen's d (Cohen, 1988), but there are many
more.
Here you will find a number of online calculators for the computation of
different effect sizes and an interpretation table at the bottom of this page. Please click on the grey bars to show the calculators:
1. Comparison of groups with equal size (Cohen's d and Glass Δ)
If the two groups have the same n, then the effect size is simply calculated by subtracting the means and dividing the result by the
pooled standard deviation. The resulting effect size is called dCohen and it represents the difference between the groups in terms of
their common standard deviation. It is used f. e. for calculating the effect for pre-post comparisons in single groups.
In case of relevant differences in the standard deviations, Glass suggests not to use the pooled standard deviation but the standard
deviation of the control group. He argues that the standard deviation of the control group should not be influenced, at least in case
of non-treatment control groups. This effect size measure is called Glass' Δ ("Glass' Delta"). Please type the data of the control
group in column 2 for the correct calculation of Glass' Δ.
Finally, the Common Language Effect Size (CLES; McGraw & Wong, 1992) is a non-parametric effect size, specifying the
probability that one case randomly draw from the one sample has a higher value than a randomly drawn case from the other
sample. In the calculator, we take the higher group mean as the point of view, but you can use (1 - CLES) to reverse the point of
reference.
Group 1 Group 2
Mean
Standard Deviation
Effect Size dCohen
Effect Size Glass' Δ
Common Language Effect Size CLES
1 von 9
This is just a print of the calculators available at
https://www.psychometrica.de/effect_size.html (Date of retrieval 10/2019)
Please visit that page to use the calculators online.
In case, you are interested in the code, please have a look at the source
code in the header of that webpage (poorly documented, though).
N
(Total number of observations in both groups)
Confidence Coefficient
Confidence Interval for dCohen
2. Comparison of groups with different sample size (
Cohen's d, Hedges' g
)
Analogously, the effect size can be computed for groups with different sample size, by adjusting the calculation of the pooled
standard deviation with weights for the sample sizes. This approach is overall identical with dCohen with a correction of a positive
bias in the pooled standard deviation. In the literature, usually this computation is called Cohen's d as well. Please have a look at
the remarks bellow the table.
The Common Language Effect Size (CLES; McGraw & Wong, 1992) is a non-parametric effect size, specifying the probability
that one case randomly draw from the one sample has a higher value than a randomly drawn case from the other sample. In the
calculator, we take the higher group mean as the point of view, but you can use (1 - CLES) to reverse the point of reference.
Additionally, you can compute the confidence interval for the effect size and chose a desired confidence coefficient (calculation
according to Hedges & Olkin, 1985, p. 86).
Group 1 Group 2
Mean
Standard Deviation
Sample Size (N)
Effect Size dCohen resp. gHedges *
Common Language Effect Size CLES**
Confidence Coefficient
Confidence Interval
*Unfortunately, the terminology is imprecise on this effect size measure: Originally, Hedges and Olkin referred to Cohen and called their corrected effect
size d as well. On the other hand, corrected effect sizes were called g since the beginning of the 80s. The letter is stemming from the author Glass (see Ellis,
2010, S. 27), who first suggested corrected measures. Following this logic, gHedges should be called h and not g. Usually it is simply called dCohen or
gHedges to indicate, it is a corrected measure.
**The Common Language Effect Size (CLES) is calculated by using the cumulative probability of divided by 1.41 via CLES
𝛷2

3. Effect size for mean differences of groups with unequal sample size within a pre-post-control design
Intervention studies usually compare the development of at least two groups (in general an experimental group and a control
group). In many cases, the pretest means and standard deviations of both groups do not match and there are a number of
possibilities to deal with that problem. Klauer (2001) proposes to compute g for both groups and to subtract them afterwards. This
way, different sample sizes and pre-test values are automatically corrected. The calculation is therefore equal to computing the
effect sizes of both groups via form 2 and afterwards to subtract both. Morris (2008) presents different effect sizes for repeated
measures designs and does a simulation study. He argues to use the pooled pretest standard deviation for weighting the
differences of the pre-post-means (so called dppc2 according to Carlson & Smith, 1999). That way, the intervention does not
influence the standard deviation. Additionally, there are weighting to correct for the estimation of the population effect size.
Usually, Klauer (2001) and Morris (2008) yield similar results.
The downside to this approach: The pre-post-tests are not treated as repeated measures but as independent data. For dependent
tests, you can use calculator 4 or 5 or 13. transform eta square from repeated measures in order to account for dependences
between measurement points.
Intervention Group Control Group
Pre Post Pre Post
Mean
Standard Deviation
Sample Size (N)
Effect Size dppc2 sensu Morris (2008)
Effect Size dKorr sensu Klauer (2001)
*Remarks: Klauer (2001) published his suggested effect size in German language and the reference should therefore be hard to retrieve for international
readers. Klauer worked in the field of cognitive trainings and was interested in the comparison of the effectivity of different training approaches. His
measure is simple and straightforward: dcorr is simply the difference between Hedge's g of two different treatment groups in pre-post research designs.
When reporting meta analytic results in international journals, it might be easier to cite Morris (2008).
4. Effect size estimates in repeated measures designs
While steps 1 to 3 target at comparing independent groups, especially in intervention research, the results are usually based on
intra-individual changes in test scores. Morris & DeShon (2008, p.111) suggest a procedure to estimate the effect size for single-
group pretest-posttest designs by taking the correlation between the pre- and post-test into account:
𝜎𝜎 21𝜌

In case, the correlation is .5, the resulting effect size equals 1. Comparison of groups with equal size (Cohen's d and Glass Δ).
Higher values lead to an increase in the effect size. Morris & DeShon (2008) suggest to use the standard deviation of the pre-test,
as this value is not influenced by the intervention. The following calculator both reports the according effect size and as well
reports the effect size based on the pooled standard deviation:
Group 1 Group 2
Mean
Standard Deviation
Correlation
Effect Size dRepeated Measures
Effect Size dRepeated Measures, pooled
N
Confidence Coefficient
Confidence Interval for dRM
Thanks to Sven van As for pointing us to this effect size.
5. Calculation of d and r from the test statistics of dependent and independent t-tests
Effect sizes can be obtained by using the tests statistics from hypothesis tests, like Student t tests, as well. In case of independent
samples, the result is essentially the same as in effect size calculation #2.
Dependent testing usually yields a higher power, because the interconnection between data points of different measurements are
kept. This may be relevant f. e. when testing the same persons repeatedly, or when analyzing test results from matched persons or
twins. Accordingly, more information may be used when computing effect sizes. Please note, that this approach largely has the
same results compared to using a t-test statistic on gain scores and using the independent sample approach (Morris & DeShon,
2002, p. 119). Additionally, there is not THE one d, but that there are different d-like measures with different meanings.
Consequently a d from an dependent sample is not directly comparable to a d from an independent sample, but yields different
meanings (see notes below table).
Please choose the mode of testing (dependent vs. independent) and specify the t statistic. In case of a dependent t test, please type
in the number of cases and the correlation between the two variables. In case of independent samples, please specify the number
of cases in each group. The calculation is based on the formulas reported by Borenstein (2009, pp. 228).
Mode of testing
Student t Value
n1
n2
r
Effect Size d
* We used the formula tc described in Dunlop, Cortina, Vaslow & Burke (1996, S. 171) in order to calculate d from dependent t-tests. Simulations proved it
to have the least distortion in estimating d: 𝑑𝑡

We would like to thank Frank Aufhammer for pointing us to this publication.
** We would like to thank Scott Stanley for pointing out the following aspect: "When selecting 'dependent' in the drop down, this calculator does not
actually calculate an effect size based on accounting for the dependency between the two variables being compared. It removes that dependency already
calculated into a t-statistic so formed. That is, what this calculator does is take a t value you already have, along with the correlation, from a dependent t-test
and removes the effect of the dependency. That is why it returns a value more like calculator 2. This calculator will produce an effect size when dependent
is selected as if you treated the data as independent even though you have a t-statistic for modeling the dependency. Some experts in meta-analysis
explicitly recommend using effect sizes that are not based on taking into account the correlation. This is useful for getting to that value when that is your
intention but what you are starting with is a t-test and correlation based on a dependent analysis. If you would rather have the effect size taking into account
the dependency (the correlation between measures), and you have the data, you should use calculator 4." (direct correspondence on 18th of August, 2019).
Further discussions on this aspect is given in Jake Westfall's blog. To sum up: The decision on which effect size to use depends on your research question
and this decision cannot be resolved definitively by the data themselves.
6. Computation of d from the F-value of Analyses of Variance (ANOVA)
A very easy to interpret effect size from analyses of variance (ANOVAs) is η2 that reflects the explained proportion variance of
the total variance. This proportion may be 13. transformed directly into d. If η2 is not available, the F value of the ANOVA can be
used as well, as long as the sample size is known. The following computation only works for ANOVAs with two distinct groups
(df1 = 1; Thalheimer & Cook, 2002):
F-Value
Sample Size of the Treatment Group
Sample Size of the Controll Group
Effect Size d
7. Calculation of effect sizes from ANOVAs with multiple groups, based on group means
In case, the groups means are known from ANOVAs with multiple groups, it is possible to compute the effect sizes f and d
(Cohen, 1988, S. 273 ff.). Prior to computing the effect size, you have to determine the minimum and maximum mean and to
calculate the between groups standard deviation σm manually:
1. compute the differences between the means of each single group and the mean of the whole sample
2. square the differences and sum them up
3. divide the sum by the number of means
4. draw the square root
𝜎


Additionally, you have to decide, which scenario fits the data best:
1. Please choose 'minimum deviation', if the group means are distributed close to the total mean.
2. Please choose 'intermediate deviation', if the means are evenly distributed.
3. Please choose 'maximum deviation', if the means are distributed mainly towards the extremes and not in the center of the
range of means.
Highest Mean (mmax)
Lowest Mean (mmin)
Between Group Std (σm)
Std (σ for the complete sample)
Number of Groups
Distribution of Means
Effect Size f
Effect Size d
8. Increase of intervention success:
The Binomial Effect Size Display (BESD)
and
Number Needed to Treat (NNT)
Measures of effect size like d or correlations can be hard to communicate, e. g. to patients. If you use r2 f. e., effects seem to be
really small and when a person does not know or understand the interpretation guidelines, even effective interventions could be
seen as futile. And even small effects can be very important, as Hattie (2007) underlines:
The effect of a daily dose of aspirin on cardio-vascular conditions only amounts to d = 0.07. However, if you look at the
consequences, 34 of 1000 die less because of cardiac infarction.
Chemotherapy only has an effect of d = 0.12 on breast cancer. According to the interpretation guideline of Cohen, the
therapy is completely ineffective, but it safes the life of many women.
Rosenthal and Rubin (1982) suggest another way of looking on the effects of treatments by considering the increase of success
through interventions. The approach is suitable for 2x2 contingency tables with the different treatment groups in the rows and the
number of cases in the columns. The BESD is computed by subtracting the probability of success from the intervention an the
control group. The resulting percentage can be transformed into dCohen.
Another measure, that is widely used in evidence based medicine, is the so called Number Needed to Treat. It shows, how many
people are needed in the treatment group in order to obtain at least one additional favorable outcome. In case of a negative value,
it is called Number Needed to Harm.
Please fill in the number of cases with a fortunate and unfortunate outcome in the different cells:
Success Failure Probability of Success
Intervention group
Control Group
Binomial Effect Size Display (BESD)
(Increase of Intervention Success)
Number Needed to Treat
rPhi
Effect Size dcohen
A conversion between NNT and other effect size measures liken Cohen's d is not easily possible. Concerning the example above,
the transformation is done via the point-biserial correlation rphi which is nothing but an estimation. It leads to a constant NNT
independent from the sample size and this is in line with publications like Kraemer and Kupfer (2006). Alternative approaches
(comp. Furukawa & Leucht, 2011) allow to convert between d and NNT with a higher precision and usually they lead to higher
numbers. The Kraemer et al. (2006) approach therefore seems to probably overestimate the effect and it seems to yield accurate
results essentially, when normal distribution of the raw values is given. Please have a look at the Furukawa and Leucht (2011)
paper for further information:
Cohen's d Number Needed to Treat (NNT)
9. Risk Ratio, Odds Ratio and Risk Difference
Studies, investigating if specific incidences occur (e. g. death, healing, academic success ...) on a binary basis (yes versus no), and
if two groups differ in respect to these incidences, usually Odds Ratios, Risk Ratios and Risk Differences are used to quantify the
differences between the groups (Borenstein et al. 2009, chap. 5). These forms of effect size are therefore commonly used in
clinical research and in epidemiological studies:
The Risk Ratio is the quotient between the risks, resp. probabilities for incidences in two different groups. The risk is
computed by dividing the number of incidences by the total number in each group and building the ratio between the
groups.
The Odds Ratio is comparable to the relative risk, but the number of incidences is not divided by the total number, but by
the counter number of cases. If f. e. 10 persons die in a group and 90 survive, than the odds in the groups would be 10/90,
whereas the risk would be 10/(90+10). The odds ratio is the quotient between the odds of the two groups. Many people find
Odds Ratios less intuitive compared to risk ratios and if the incidence is uncommon, both measures are roughly
comparable. The Odds Ratio has favorable statistical properties which makes it attractive for computations and is thus
frequently used in meta analytic research. Yul e 's Q - a measure of association - transforms Odds Ratios to a scale ranging
from -1 to +1.
The Risk Difference is simply the difference between two risks. Compared to the ratios, the risks are not divided but
subtracted from each other. For the computation of Risk Differences, only the raw data is used, even when calculating
variance and standard error. The measure has a disadvantage: It is highly influenced by changes in base rates.
When doing meta analytic research, please use LogRiskRatio or LogOddsRatio when aggregating data and delogarithmize the sum
finally.
Incidence no Incidence N
Treatment
Control
Risk Ratio Odds Ratio Risk Difference
Result
Log
Estimated
Variance
VVLogRiskRatio VLogOddsRatio VRiskDifference
Estimated
Standard
Error SE SELogRiskRatio SELogOddsRatio SERiskDifference
Yule ' s Q
10. Effect size for the difference between two correlations
Cohen (1988, S. 109) suggests an effect size measure with the denomination q that permits to interpret the difference between two
correlations. The two correlations are transformed with Fisher's Z and subtracted afterwards. Cohen proposes the following
categories for the interpretation: <.1: no effect; .1 to .3: small effect; .3 to .5: intermediate effect; >.5: large effect.
Correlation r1
Correlation r2
Cohen's q
Interpretation
Especially in meta analytic research, it is often necessary to average correlations or to perform significance tests on the difference
between correlations. Please have a look at our page Testing the Significance of Correlations for on-line calculators on these
subjects.
11. Effect size calculator for non-parametric tests: Mann-Whitney-U, Wilcoxon-W and Kruskal-Wallis-H
Most statistical procedures like the computation of Cohen's d or eta;2 at least interval scale and distribution assumptions are
necessary. In case of categorical or ordinal data, often non-parametric approaches are used - in the case of statistical tests for
example Wilcoxon or Mann-Whitney-U. The distributions of the their test statistics are approximated by normal distributions and
finally, the result is used to assess significance. Accordingly, the test statistics can be transformed in effect sizes (comp. Fritz,
Morris & Richler, 2012, p. 12; Cohen, 2008). Here you can find an effect size calculator for the test statistics of the Wilcoxon
signed-rank test, Mann-Whitney-U or Kruskal-Wallis-H in order to calculate η2. You alternatively can directly use the resulting z
value as well:
Test
Test statistics *
n2
n2
Eta squared (η2)
dCohen**
* Note: Please do not use the sum of the ranks but instead directly type in the test statistics U, W or z from the inferential tests. As Wilcoxon relies on
dependent data, you only need to fill in the total sample size. For Kruskal-Wallis please as well specify the total sample size and the number of groups. For
z, please fill in the total number of observations (either the total sample size in case of independent tests or for dependent measures with single groups the
number of individuals multiplied with the number of assessments; many thanks to Helen Askell-Williams for pointing us this aspect).
** Transformation of η2 is done with the formulas of 13. Transformation of the effect sizes d, r, f, Odds Ratio and η2.
12. Computation of the pooled standard deviation
In order to compute Conhen's d, it is necessary to determine the mean (pooled) standard deviation. Here, you will find a small
tool that does this for you. Different sample sizes are corrected as well:
Group 1 Group 2
Standard Deviation
Sample size (N)
Pooled Standard Deviation spool
13. Transformation of the effect sizes
d
,
r
,
f
,
Odds Ratio
,
η
2
and
Common Language Effect Size (CLES)
Please choose the effect size, you want to transform, in the drop-down menu. Specify the magnitude of the effect size in the text
field on the right side of the drop-down menu afterwards. The transformation is done according to Cohen (1988), Rosenthal
(1994, S. 239), Borenstein, Hedges, Higgins, and Rothstein (2009; transformation of d in Odds Ratios) and Dunlap (1994;
transformation in CLES).
Effect Size
d0.8729
r0.4
η20.16
f0.4364
Odds Ratio 4.8706
Common Language Effect Size CLES 0.63
Number Needed to Treat (NNT) 2.1603
Remark: Please consider the additional explanations concerning the transform from d to Number Needed to Treat in the section BESD and NNT. The
conversion into CLES is based on r with the formula specified by Dunlap (1994): CLES

Π.5
14. Computation of the effect sizes
d
,
r
and
η
2
from
χ
2
- and
z
test statistics
The χ2 and z test statistics from hypothesis tests can be used to compute d and r(Rosenthal & DiMatteo, 2001, p. 71; comp. Elis,
2010, S. 28). The calculation is however only correct for χ2 tests with one degree of freedom. Please choose the tests static
measure from the drop-down menu and specify the value and N. The transformation from d to r and η2 is based on the formulas
used in the prior section (13).
Test Statistic
N
d
r
η2
15. Table of interpretation for different effect sizes
Here, you can see the suggestions of Cohen (1988) and Hattie (2009 S. 97) for interpreting the magnitude of effect sizes. Hattie
refers to real educational contexts and therefore uses a more benignant classification, compared to Cohen. We slightly adjusted
the intervals, in case, the interpretation did not exactly match the categories of the original authors.
dr*η2Interpretation sensu
Cohen (1988)
Interpretation sensu
Hattie (2007)
< 0 < 0 - Adverse Effect
0.0 .00 .000 No Effect Developmental effects
0.1 .05 .003
0.2 .10 .010
Small Effect Teacher effects
0.3 .15 .022
0.4 .2 .039
Zone of desired
effects
0.5 .24 .060
Intermediate Effect0.6 .29 .083
0.7 .33 .110
0.8 .37 .140
Large Effect0.9 .41 .168
≥ 1.0 .45 .200
* Cohen (1988) reports the following intervals for r: .1 to .3: small effect; .3 to .5: intermediate effect; .5 and higher: strong effect
Literature
Borenstein (2009). Effect sizes for continuous data. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of
research synthesis and meta analysis (pp. 221-237). New York: Russell Sage Foundation.
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis, Chapter 7: Converting
Among Effect Sizes . Chichester, West Sussex, UK: Wiley.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2. Auflage). Hillsdale, NJ: Erlbaum.
Cohen, B. (2008). Explaining psychological statistics (3rd ed.). New York: John Wiley & Sons.
Dunlap, W. P. (1994). Generalizing the common language effect size indicator to bivariate normal correlations. Psychological
Bulletin, 116(3), 509-511. doi: 10.1037/0033-2909.116.3.509
Dunlap, W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or
repeated measures designs. Psychological Methods, 1, 170-177.
Elis, P. (2010). The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results.
Cambridge: Cambridge University Press.
Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of
Experimental Psychology: General, 141(1), 2-18. https://doi.org/10.1037/a0024338
Furukawa, T. A., & Leucht, S. (2011). How to obtain NNT from Cohen's d: comparison of two methods. PloS one, 6, e19070.
Hattie, J. (2009). Visible Learning. London: Routledge.
Hedges, L. & Olkin, I. (1985). Statistical Methods for Meta-Analysis. New York: Academic Press.
Klauer, K. J. (2001). Handbuch kognitives Training. Göttingen: Hogrefe.
McGraw, K. O., & Wong, S. P. (1992). A common language effect size statistic. Psychological bulletin, 111(2), 361-365.
Morris, S. B., & DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-
groups designs. Psychological Methods, 7(1), 105-125. https://doi.org/10.1037//1082-989X.7.1.105
Morris, S. B. (2008). Estimating Effect Sizes From Pretest-Posttest-Control Group Designs. Organizational Research Methods,
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Rosenthal, R. (1994). Parametric measures of effect size. In H. Cooper & L. V. Hedges (Eds.), The Handbook of Research
Synthesis (231-244). New York, NY: Sage.
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Annual Review of Psychology, 52(1), 59-82. doi:10.1146/annurev.psych.52.1.59
Thalheimer, W., & Cook, S. (2002, August). How to calculate effect sizes from published research articles: A simplified
methodology. Retrieved March 9, 2014 from http://work-learning.com/effect_sizes.htm.
In case you need a reference to this page in a scientific paper, please use the following citation:
Lenhard, W. & Lenhard, A. (2016). Calculation of Effect Sizes. Retrieved from: https://www.psychometrica.de
/effect_size.html. Dettelbach (Germany): Psychometrica. DOI: 10.13140/RG.2.1.3478.4245
Copyright © 2017 Drs. Alexandra & Wolfgang Lenhard

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... We also calculated effect sizes. For the differences in the proportions of cases with coercive measures, we calculated unadjusted risk ratios (RR) and 95% confidence intervals (95%-CI), and for the differences in the median cumulated duration of coercive measures and for the difference in the SOAS-R score, we calculated effect sizes eta squared and converted them into common language effect sizes Cohen's d [17,18]. Analyses were done with IBM 1 SPSS 1 Statistics Version 27, Microsoft 1 Excel 1 2013. ...
... Analyses were done with IBM 1 SPSS 1 Statistics Version 27, Microsoft 1 Excel 1 2013. Effect sizes for duration of coercive measures and for the difference in the SOAS-R score were calculated online [18]. ...
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Objective To examine whether the pandemic in 2020 caused changes in psychiatric hospital cases, the percentage of patients exposed to coercive interventions, and aggressive incidents. Methods We used the case registry for coercive measures of the State of Baden-Wuerttemberg, comprising case-related data on mechanical restraint, seclusion, physical restraint, and forced medication in each of the State’s 31 licensed hospitals treating adults, to compare data from 2019 and 2020. Results The number of cases in adult psychiatry decreased by 7.6% from 105,782 to 97,761. The percentage of involuntary cases increased from 12.3 to 14.1%, and the absolute number of coercive measures increased by 4.7% from 26,269 to 27,514. The percentage of cases exposed to any kind of coercive measure increased by 24.6% from 6.5 to 8.1%, and the median cumulative duration per affected case increased by 13.1% from 12.2 to 13.8 hrs, where seclusion increased more than mechanical restraint. The percentage of patients with aggressive incidents, collected in 10 hospitals, remained unchanged. Conclusions While voluntary cases decreased considerably during the pandemic, involuntary cases increased slightly. However, the increased percentage of patients exposed to coercion is not only due to a decreased percentage of voluntary patients, as the duration of coercive measures per case also increased. The changes that indicate deterioration in treatment quality were probably caused by the multitude of measures to manage the pandemic. The focus of attention and internal rules as well have shifted from prevention of coercion to prevention of infection.
... The effect size was estimated using the mean differences between the baseline measurements and follow-ups from this pilot study and from a previous paper by Barnes et al. [26]. The effects from an RCT conducted by Wong et al. [15] were also accounted for by the f value from an analysis of variance [27]. ...
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Background: Recent work has examined an association between leg length discrepancy (LLD) and low back pain. Myofascial release (MFR) techniques are thought to be frequently applied in the treatment of chronic and acute low back pain (LBP). The purpose of this study was to evaluate a practical measure of LLD and the feasibility of an MFR technique in a randomised controlled trial (RCT). Methods: In 12 subjects (7 women and 5 men) with aLBP and LLD greater than 3 mm, an MFR technique was performed on the thoracolumbar fascia. At the baseline, after the intervention, and at follow-up, LLD was measured using a cross line laser and finger-to-floor distance (FFD), and pain was measured with a visual analogue scale (VAS). Patients completed a questionnaire after follow-up to assess the acceptability of the study procedure. The therapist evaluated the methods in terms of their feasibility. Results: LLD measurement and MFR treatment required little time and few resources. Participants agreed to the study procedure with moderate to high acceptance. The LLD decreased by 5 mm after treatment and by 4 mm at follow-up. The VAS showed a reduction in pain of 17.50 mm at follow-up but not immediately after treatment. Conclusion: The measurement of LLD is applicable in daily osteopathic practice, but it cannot be assumed to be a valid method for an RCT. Validated methods such as video raster stereography are therefore recommended. Comprehensive RCTs to study the effects of MFR intervention on leg length are feasible.
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