Addressing cultural and knowledge barriers to enable preclinical sex inclusive research
- Novartis Biomedical Research, United States
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, United Kingdom
- Faculty of Medicine & Dentistry, Queen Mary University of London, United Kingdom
- University of Glasgow, United Kingdom
- Obafemi Awolowo University and Obafemi Awolowo University Teaching Hospitals Complex, Nigeria
- Institute of Pharmacy and Biomedical Science, University of Strathclyde, United Kingdom
Figures
Figure 1
Exploration of the perceived barriers and benefits of inclusion of males and females in in vivo research from study 1.
Evaluation of survey data collected in study 1 with 39 participants in the baseline group, 51 in the interested group, and 15 in the intervention group. (A) Percentage of study participants who selected each barrier, displayed by treatment group. (B) Percentage of study participants who selected each benefit, displayed by treatment group. (C) Percentage of study participants who selected each barrier in the general population (baseline and interested group combined). (D) Percentage of study participants who selected each benefit in the general population (baseline and interested group combined). A Pearson’s chi-squared test was used to compare the proportion of participants selecting each barrier/benefit between the treatment groups. Statistical significance is highlighted with a horizontal bar and if the p-value is less than 0.05, it is flagged with one star (*). If the p-value is less than 0.01, it is flagged with 2 stars (**). If a p-value is less than 0.001 it is flagged with 3 stars (***).
Figure 2
Exploration of the perceived barriers and benefits on the topic of inclusion of females and males in in vivo research for study 2.
Evaluation of data collected from study 2 with 29 participants completing the barrier question pre-intervention and 28 in the post-intervention. (A) Percentage of study participants who selected each barrier, pre- and post-intervention, for the barriers targeted by the intervention. (B) Percentage of study participants who selected all other barriers, displayed pre-and post-intervention. (C) Percentage of study participants that selected each benefit, displayed pre- and post-intervention. (D) Comparison of the percentage of study participants selecting benefits in study 1 general population (baseline plus interested group) versus study 2 pre-intervention group. (E) Comparison of the percentage of study participants selecting barriers in study 1 general population (baseline plus interested groups) versus study 2 pre-intervention group. A McNemar’s test of association was used to compare the proportions between the pre- and post-intervention data in study 2. A chi-squared test was used to compare proportions between the two studies. Statistical significance is highlighted with a horizontal bar, and if the p-value is less than 0.05, it is flagged with one star (*). If the p-value is less than 0.01, it is flagged with 2 stars (**). If a p-value is less than 0.001, it is flagged with 3 stars (***).
Figure 3
Exploration of intent for significant and critical predictor variables for study 1 data.
A full model, with all potential predictors and demographics, was fitted to explore the variation in intent and assess for evidence of predictive behaviour (N=39 Baseline group, 51 Interested group and 15 intervention group). The baseline group was set as the reference group. If main effects were significant, the variation by treatment group was explored with Tukey post hoc testing. (A) Relationship between intention and attitude. (B) Relationship between intention and behavioural control. (C) Relationship between intention and social norm. For panels A, B, and C, the grey area indicates the 95% confidence interval for the fitted linear relationship (blue) and the text indicates the statistical significance of the relationship. (D) Model estimated means (Least Square Means) for each treatment group with a standard error bar estimated from the model. Vertical bars represent the planned comparison between groups, with * representing statistical significance <0.05. (E) Violin plot showing the distribution of intent as a function of the ability to influence the design. Points indicate individual study participants, and the red box indicates the calculated mean for each group. The text indicates the statistical assessment for that variable.
Figure 4
Exploration of intent for significant and critical predictor variables for study 2 data.
A full model, with all potential predictors and demographics, was fitted to explore the variation in intent and assess for evidence of predictive behaviour. (A) Relationship between intention and attitude. (B) Relationship between intention and behavioural control. (C) Relationship between intention and social norm. For panels A, B, and C, the grey area indicates the 95% confidence interval for the fitted linear relationship (blue) and the text indicates the statistical significance of the relationship. (D) Exploration of intention between pre- and post-intervention where a line links each individual participants score and shaded area indicates the density of the Box-Cox transformed measure of intent. (E) Relationship between intention and age. For graphs A, B, C, and E, the grey area indicates the 95% confidence interval for the fitted linear relationship (blue) for Box-Cox transformed intent (y-axis) against key predictors in the TPB model (x-axis) (A, B, C) and significant predictor ‘age’ (E).
Figure 5
Exploration of intervention impact on the proportion of correctly answered knowledge questions.
(A) Study 1: Cumulative knowledge score (cumulative questions answered correctly) displayed by treatment group (Baseline: N=39, interest: N=51 and intervention: N=15). Statistical significance assessed with a Poisson regression. (B) Study 1: Percentage of correct answers for each question, displayed by treatment group (Baseline: N=39, interest: N=51, and intervention: N=15). Statistical significance assessed with a Pearson’s chi-squared test. (C) Study 2: Impact of intervention on the cumulative knowledge score. Where a line links each individual participants score, and shaded area indicates the density of the Box-Cox transformed measure of intent. Statistical significance assessed with a paired t-test (N=26 with pre and post data available). (D) Study 2: Percentage correct answers for each question, displayed by pre- and post-intervention group. (Pre: N=29, Post: N=28). Statistical significance assessed with McNemar’s test of association. Statistical significance shown as * for p-value<0.05, ** for p<0.01 and *** for p<0.001.
Tables
Table 1
Workshop Intervention construct.
| Section | Content |
|---|---|
| Lecture | Terminology Reflection that clinical sex matters. Exploration of the status of sex inclusion in preclinical research. Exploration of what is sex-inclusive research. The role of factorial analysis in sex-inclusive research. |
| Interactive element | Multiple choice questions with voting and discussion on appropriate analysis strategy and understanding factorial output. |
| Lecture | Exploration of the perceived barriers to sex-inclusive research. |
| Evaluation tool | Introduction of the Sex Inclusive Research Framework (SIRF) which can be used to evaluate research proposals from a sex inclusive perspective. |
| Interactive element | Multiple choice classification using the SIRF to classify justifications given for one-sex designs. |
Table 2
Statistical model output for the full model for study 1 data exploring the predictors ability to explain variation in average intent.
| Source | Nparm | Df | Sum of Squares | F Ratio | Prob >F | Significance | Eta2 |
|---|---|---|---|---|---|---|---|
| Attitude | 1 | 1 | 18.5650 | 34.9548 | <.0001 | *** | 0.15 |
| Beh_Control | 1 | 1 | 0.0814 | 0.1534 | 0.6964 | 0.00 | |
| Soc_Norm | 1 | 1 | 12.2229 | 23.0137 | <.0001 | *** | 0.10 |
| Treatment_Group | 2 | 2 | 3.8643 | 3.6380 | 0.0311 | * | 0.03 |
| Age | 1 | 1 | 0.0995 | 0.1874 | 0.6664 | 0.00 | |
| Gender | 2 | 2 | 2.6196 | 2.4662 | 0.0918 | 0.02 | |
| Geography | 3 | 3 | 0.1955 | 0.1227 | 0.9464 | 0.00 | |
| Year_Work | 1 | 1 | 0.1828 | 0.3442 | 0.5592 | 0.00 | |
| Type_Work | 3 | 3 | 1.1958 | 0.7505 | 0.5255 | 0.01 | |
| Education | 2 | 2 | 2.9339 | 2.7621 | 0.0696 | 0.02 | |
| Stats_Training | 2 | 2 | 0.3810 | 0.3587 | 0.6998 | 0.00 | |
| Factorial_Fam | 1 | 1 | 0.0332 | 0.0625 | 0.8033 | 0.00 | |
| Factorial_Incor | 1 | 1 | 0.2614 | 0.4921 | 0.4852 | 0.00 | |
| Ability_Influence | 1 | 1 | 2.3253 | 4.3782 | 0.0398 | * | 0.02 |
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Where Beh_control represents the average behavioural control score, Soc_norm the average social normal score, Year_Work represented the number of years the participants have worked in animal research, Type_Work represents the type of research conducted by the participant, Education the highest level of education obtained, Stats_Training represents the level of statistical training received, Factorial_Fam represents how familiar the participants were with factorial experimental design, Factorial_Incor represents how often the participants incorporated males and females into their experiments while studying an intervention, attitude represents the average attitude score, and Ability_Influence represents how often the participants were involved or could influence the planning of experiments involving animals. Nparm stands for the number of parameters, Df represents the degrees of freedom, and Prob >F represents the p-value associated with the F ratio. Statistical significance shown as * for p-value<0.05, ** for p<0.01 and *** for p<0.001.
Table 3
Statistical model output for the full model for study 2 data exploring the predictors ability to explain variation in average intent.
| Source | Nparm | Df | Df Denominator | F Ratio | Prob >F | Significance | Eta2 |
|---|---|---|---|---|---|---|---|
| Attitude | 1 | 1 | 38.78 | 0.0268 | 0.8708 | 6.91e-04 | |
| Beh_Control | 1 | 1 | 42.47 | 1.9080 | 0.1744 | 0.04 | |
| Soc_Norm | 1 | 1 | 29.15 | 19.658 | 0.0001 | *** | 0.40 |
| Intervention | 1 | 1 | 40.06 | 0.0758 | 0.7845 | 1.89e-03 | |
| Age | 1 | 1 | 28.2 | 5.3428 | 0.0283 | * | 0.16 |
| Gender | 2 | 2 | 33.93 | 0.2973 | 0.7447 | 0.02 | |
| Ability_Influence | 1 | 1 | 27.91 | 2.2618 | 0.1438 | 0.07 | |
| Factorial_Fam | 1 | 1 | 42.98 | 0.3822 | 0.5397 | 8.81e-03 | |
| Factorial_Incor | 1 | 1 | 35.72 | 0.0725 | 0.7893 | 2.03e-03 |
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Where Beh_control represent the average behavioural control score, Soc_norm the average social normal score, Year_Work represented the number of years the participants have worked in animal research, Type_Work represents the type of research conducted by the participant, Education the highest level of education obtained, Stats_Training represents the level of statistical training received, Factorial_Fam represents how familiar the participants were with factorial experimental design, Factorial_Incor represents how often the participants incorporated males and females into their experiments while studying an intervention, attitude represents the average attitude score, and Ability_Influence represents how often the participants were involved or could influence the planning of experiments involving animals. Nparm stands for the number of parameters, Df represents the degrees of freedom, and Prob > F represent the p-value associated with the F ratio. Statistical significance is flagged with one star (*) if the p-value is less than 0.05, with 2 stars (**) if less than 0.01, and with 3 stars (***) if less than 0.001.
Appendix 1—table 1
Study 1.
| Ability_Influence | Factorial_Fam | Factorial_Incor | Attitude | Beh_Control | Soc_Norm | |
|---|---|---|---|---|---|---|
| Ability_Influence | 1.0000 | 0.3499 | 0.2581 | –0.0138 | 0.2218 | 0.1514 |
| Factorial_Fam | 0.3499 | 1.0000 | 0.3317 | 0.2172 | 0.2371 | 0.2341 |
| Factorial_Incor | 0.2581 | 0.3317 | 1.0000 | 0.2539 | 0.4496 | 0.4384 |
| Attitude | –0.0138 | 0.2172 | 0.2539 | 1.0000 | 0.2081 | 0.1555 |
| Beh_Control | 0.2218 | 0.2371 | 0.4496 | 0.2081 | 1.0000 | 0.3691 |
| Soc_Norm | 0.1514 | 0.2341 | 0.4384 | 0.1555 | 0.3691 | 1.0000 |
Appendix 1—table 2
Study 2.
| Ability_Influence | Factorial_Fam | Factorial_Incor | Attitude | Beh_Control | Soc_Norm | Age | |
|---|---|---|---|---|---|---|---|
| Ability_Influence | 1.0000 | 0.1361 | 0.1862 | 0.1305 | 0.3818 | 0.2926 | 1.0000 |
| Factorial_Fam | 0.1361 | 1.0000 | –0.2319 | 0.0714 | 0.3041 | 0.2671 | 0.1361 |
| Factorial_Incor | 0.1862 | –0.2319 | 1.0000 | –0.0689 | 0.1139 | 0.0407 | 0.1862 |
| Attitude | 0.1305 | 0.0714 | –0.0689 | 1.0000 | 0.2664 | 0.0710 | 0.1305 |
| Beh_Control | 0.3818 | 0.3041 | 0.1139 | 0.2664 | 1.0000 | 0.1624 | 0.3818 |
| Soc_Norm | 0.2926 | 0.2671 | 0.0407 | 0.0710 | 0.1624 | 1.0000 | 0.2926 |
| Age | 0.2047 | –0.0511 | –0.0616 | –0.3604 | 0.1078 | 0.1659 | 0.2047 |
Appendix 2—table 1
Test of association of the perceived barriers between treatment groups for study 1.
This survey question provided several pre-defined options and ability to enter a free-text option. Participants were asked to choose all that applied. Exploration of the free-text has grouped the barriers into three additional categories: convention, logistic, and none or no barriers. To test for a statistically significant difference (association) between the treatment groups, a Pearson’s Chi-square test was applied for all options where the total N>10.
| Barrier offered | Total (count) possible N=105 | Baseline (count) possible N=39 | Interested (count) possible N=51 | Intervention (count) possible N=15 | Test of association test statistic & p-value |
|---|---|---|---|---|---|
| Cost | 44 | 14 | 24 | 6 | Χ2 = 1.157; p = 0.561 |
| Male animals are more likely to fight and may lead to premature euthanasia | 34 | 8 | 21 | 5 | Χ2 = 4.317; p = 0.115 |
| Female animals are more variable | 29 | 6 | 18 | 5 | Χ2 = 4.668; p = 0.097 |
| Complexity of experimental design | 28 | 7 | 18 | 3 | Χ2 = 3.789; p = 0.149 |
| Model behavior may be different in the other sex | 27 | 10 | 14 | 3 | Χ2 = 0.337; p = 0.844 |
| Sample size concerns | 24 | 7 | 13 | 4 | Χ2 = 0.857; p = 0.651 |
| Not relevant to the research question | 19 | 9 | 9 | 1 | Χ2 = 1.98; p = 0.371 |
| Availability of sample/test material | 12 | 2 | 7 | 3 | Χ2 = 2.884; p = 0.236 |
| Welfare issues | 5 | 2 | 2 | 1 | NA |
| Data analysis concerns | 0 | 0 | 0 | 0 | NA |
| Other - Convention | 7 | 2 | 3 | 2 | NA |
| Other – Logistics | 1 | 1 | 0 | 0 | NA |
| Other - None | 3 | 1 | 0 | 2 | NA |
Appendix 3—table 1
Test of association of the perceived benefits between treatment groups for study 1.
This question provided several pre-defined options and the ability to enter a free-texted option. Participants were asked to choose all that applied. No free-text advantages were provided by survey takers for this question. To test for a statistically significant difference (association) between the treatment groups, a Pearson’s chi-square test of association was applied for all options where the total N>10.
| Benefits offered | Total possible N=105 | Baseline (count) possible N=39 | Interested (count) possible N=51 | Intervention (count) possible N=15 | Test of association test statistic & p-value |
|---|---|---|---|---|---|
| Understanding sex differences | 91 | 33 | 44 | 14 | Χ2=0.726; p=0.695 |
| Translatability | 79 | 29 | 35 | 15 | Χ2=6.149; p=0.046 |
| Efficient use of all animals from breeding | 42 | 12 | 18 | 12 | Χ2=11.855; p=0.0026 |
| Reproducibility | 42 | 17 | 16 | 9 | Χ2=4.291; p=0.117 |
| 3Rs – Reduction | 33 | 11 | 14 | 8 | Χ2=3.902; p=0.142 |
| Animal welfare | 22 | 10 | 7 | 5 | Χ2=3.48; p=0.176 |
| Other | 0 | 0 | 0 | 0 | NA |
Appendix 4—table 1
Test of association of the perceived barriers between pre- and post-intervention for study 2.
This question provided several pre-defined options and the ability to enter a free-texted option. Participants were asked to choose all that applied. No free-text advantages were provided by survey takers for this question. To test for a statistically significant difference (association) between the treatment groups, a McNemar’s test was applied for all options where the total N>10.
| Barrier offered | Pre-Intervention (count)Possible N=29 | Post-Intervention (count)Possible N=28 | McNemar’s testTest statistic & p value |
|---|---|---|---|
| Cost | 15 | 8 | Χ2=3.57; P=0.060 |
| Complexity of experimental design | 14 | 5 | Χ2=5.33; P=0.021 |
| Sample size concerns | 13 | 4 | Χ2=4.45; P=0.035 |
| Model behaviour may be different in the other sex | 12 | 2 | Χ2=5.44; P=0.020 |
| Availability of sample/test material | 10 | 9 | Χ2=0.0; P=1.0 |
| Female animals are more variable | 9 | 2 | Χ2=5.0; P=0.025 |
| Experiment would take longer | 4 | 5 | NA |
| Male animals are more likely to fight and may lead to premature euthanasia | 3 | 2 | NA |
| Convention | 2 | 4 | NA |
| Data analysis concerns | 2 | 4 | NA |
| Logistics | 3 | 4 | NA |
| Welfare issues | 2 | 1 | NA |
| Not relevant to the research question | 0 | 4 | NA |
| Free-text: Sponsor request | 0 | 1 | NA |
| Free-text: Model induction issue | 0 | 1 | NA |
Appendix 5—table 1
Test of association of the perceived benefits between pre- and post-intervention for study 2.
This survey question provided several pre-defined options and ability to enter a free-texted option. Participants were asked to choose all that applied. Participants were given the option of a free-text response, but none were submitted. To test for a statistically significant difference (association) between the pre- and post-measures, a McNemar’s test was applied for all options where the total N>10. This test accounts for the repeat nature of the data which required the list-wise deletion of those individuals with missing data.
| Advantages offered | Pre-intervention (count) possible N=29 | Post-intervention (count) possible N=28 | McNemar’s test- statistic & p-value |
|---|---|---|---|
| Understanding sex differences | 26 | 25 | Χ2=0.0; p=1.0 |
| Translatability | 21 | 22 | Χ2=1.0; p=0.32 |
| Efficient use of all animals from breeding | 13 | 17 | Χ2=1.6; p=0.21 |
| 3Rs - Reduction | 8 | 13 | Χ2=2.78; p=0.096 |
| Reproducibility | 6 | 13 | Χ2=6.0; p=0.014 |
| Animal welfare | 3 | 5 | NA |
Appendix 6—table 1
Test of association for study 1 for each knowledge question.
| Question(Answer) | Total (Baseline & Interest)N=90 | Baseline(% correct)N=39 | Interest(% correct)N=51 | Intervention (% correct)N=15 | Test of association[Baseline v Interest]Test statistic & p-value | Test of association[across the 3 treatment groups]test statistic & p-value |
|---|---|---|---|---|---|---|
| Do you think inclusion of both sexes requires doubling a study’s sample size? (No) | 20 (30.3%) | 13 (33%) | 7 (14%) | 10 (67%) | Χ2=5.973; p=0.014 | Χ2=16.85; p=0.00021 |
| Do you think sex influences data variability, therefore, when you include both sexes, more animals are needed? (No) | 9 (10%) | 5 (13%) | 4 (8%) | 6 (60%) | Χ2 = 0.608; p=0.4355 | Χ2=7.994; p=0.01837 |
| When analyzing in vivo data collected from both sexes, do you think sex should be included in the statistical model? (Yes) | 68 (75.6%) | 34 (87%) | 34 (67%) | 12 (80%) | Χ2=5.035; p=0.0248 | Χ2 = 5.266; p=0.072 |
| When analyzing in vivo data, do you think data from the two sexes should be pooled (combined) for an intervention into a single group for the analysis? (No) | 17 (18.9%) | 6 (15%) | 11 (21%) | 6 (40%) | Χ2 = 0.552; p=0.457 | Χ2 = 3.844; p=0.1463 |
| When analyzing in vivo data collected from both sexes, do you think the analysis should be run independently for each sex through separate statistical tests? (No) | 18 (20%) | 7 (18%) | 11 (21%) | 8 (53%) | Χ2 = 0.181; p=0.671 | Χ2=7.823; p=0.020 |
Appendix 7—table 1
Test of association for study 2 for each knowledge question.
| Question(Answer) | Pre(% correct)N=29 | Post(% correct)N=28 | McNemar’s test of associationtest statistic & p-value |
|---|---|---|---|
| Do you think inclusion of both sexes requires doubling a study’s sample size? (No) | 6 (21%) | 17 (61%) | Χ2=7.14; p=0.007 |
| Do you think sex influences data variability, therefore, when you include both sexes, more animals are needed? (No) | 2 (7%) | 13 (46%) | Χ2=9.31; p=0.0023 |
| When analyzing in vivo data collected from both sexes, do you think sex should be included in the statistical model? (Yes) | 21 (72%) | 21 (75%) | Χ2 = 0.143; p=0.71 |
| When analyzing in vivo data collected from both sexes, do you think the analysis should be disaggregated (for example, run a t-test comparing control and intervention on the male data and then running a separate test on the data from females)? (No) | 8 (28%) | 22 (79%) | Χ2=8.0; p=0.0047 |
| When analyzing in vivo data, do you think data from the two sexes should be pooled (combined) for the analysis (for example run a single t-test comparing control and intervention group)? (No) | 5 (17%) | 18 (64%) | Χ2=10.29; p=0.0013 |
Appendix 8—table 1
Study 1 free-text response.
| Statement | Classified | Group |
|---|---|---|
| Biomed staff generally keep the male rats for experimental use. Generally, I think because it is the way it has been done. | Other - Convention | Baseline |
| Convention | Other -Convention | Baseline |
| This is how my PI is doing it for many years | Other -Convention | Baseline |
| My supervisors | Other – Convention | Baseline |
| All historical data generated in make animals in a very narrow field | Other -Convention | Baseline |
| None | Other – None | Baseline |
| Logistics (duration of experiments) | Other - Logistics | Baseline |
| Larger male is preferred for implant of device | Other - Logistics | Baseline |
| After the workshop, I no longer have concerns about including both sexes in future experiments. | Other - None | Intervention |
| Expectation of boss! | Other - Convention | Intervention |
| Regulatory standard designs that are traditionally used | Other - Convention | Intervention |
| Cost of female animals | Other - Logistic | Intervention |
| None | Other – None | Intervention |
Appendix 8—table 2
Study 2 free-text response.
| Statement | Classified | Group |
|---|---|---|
| Availability of transgenics that are bred | Other - Logistic | Pre |
| Experimental model phenotype | Other - Logistic | Pre |
| We have to stagger groups in order to test all animals | Other - Logistic | Post |
| Disease exists in female (humans), however, experimental model in female mice protected | Other - Logistic | Post |
| Need a better understanding of stats - already look at genotype, time, and treatment intersections, not sure how to add sex as another variable | Other - Logistic | Post |
| Sponsor request | Other - Convention | Post |
Additional files
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Supplementary file 1
Summary of demographic and potential predictors between groups for all survey 1 contributors who met the inclusion criteria.
The demographic information included is for the full 105 participants who met the inclusion criteria. While all 105 met the inclusion criteria, seven participants left the question about age blank. As age was included as a predictor in the analysis of intent, the missing values were managed with listwise deletion, assuming missing at random, reducing the dataset size to 98 participants (N=35 baseline, N=48 interested, and N=15 intervention). To test for a statistically significant difference (association) between the treatment groups, a Pearson’s Chi-square test was used for categorical variables, ordered logistic regression for nominal variables, and ANOVA test for continuous variables. Institute type was collected as a demographic, the resulted population sampled was predominately academic and this variable was therefore removed from downstream analysis due to the lack of predictive ability to assess institute type on the outcome of interest. The abbreviation name in bracket, within the demographic column, indicates the term used within the statistical model and associated output.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp1-v1.xlsx
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Supplementary file 2
Summary of demographic and potential predictors for all participants who met the inclusion criteria for Study 2.
The demographic information is for the full 31 participants who met the inclusion criteria in study 2. While 31 unique individuals met the criteria, an individual may not have responded to both surveys (N=29 pre-survey and N=28 in the post-survey). For instance, two participants did not meet the inclusion criteria for the pre-survey but met the criteria for the post-intervention survey. For the intention analysis, some missing data was observed in the demographic data. To reduce survey burden, the post-survey only included one demographic question (the participant’s age) to support alignment of data, just in case duplicate initials were used as an identifier. Two responders did not include the age information in the study and were managed with listwise deletion, assuming missing at random. The abbreviation name in bracket, within the demographic column, indicates the term used within the statistical model and associated output. For McNemar’s paired analysis, we conducted listwise deletion, assuming missing at random, reducing the dataset size to 26.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp2-v1.xlsx
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Supplementary file 3
Cumulative knowledge score for study 1.
For each participant of study 1, the cumulative knowledge score is reported along with metadata of treatment group and reported gender. Participant identity is masked.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp3-v1.xlsx
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Supplementary file 4
Pre- and post-cumulative knowledge scores for study 2.
For each participant in study 2, the pre- and post-cumulative knowledge score is reported. Participant identity is masked.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp4-v1.xlsx
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Supplementary file 5
Study 1 survey.
The survey that was used in study 1 for all participants.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp5-v1.pdf
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Supplementary file 6
Pre-workshop survey.
The survey that was used in study 2 for all participants prior to the intervention.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp6-v1.pdf
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Supplementary file 7
Postworkshop survey.
The survey that was used in study 2 for all participants post the intervention.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp7-v1.pdf
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Supplementary file 8
Workshop training material.
The training material used in study 2.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp8-v1.pdf
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Supplementary file 9
Cumulative knowledge score for study 1.
For each participant of study 1, the cumulative knowledge score is reported alone with meta data of treatment group and reported gender. Participant identity is masked.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp9-v1.xlsx
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Supplementary file 10
Pre- and post-cumulative knowledge scores for study 2.
For each participant of study 2, the pre and post cumulative knowledge score is reported. Participant identity is masked.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp10-v1.xlsx
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Supplementary file 11
Intention analysis for both study 1 and 2.
The data and SAS code which can reproduce the analysis of intention for study 1 and study 2.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp11-v1.pdf
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Supplementary file 12
Analysis of cumulative knowledge score for study 1.
The Rmarkdown output from the analysis of the cumulative knowledge score for study 1.
- https://cdn.elifesciences.org/articles/106545/elife-106545-supp12-v1.pdf
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MDAR checklist
- https://cdn.elifesciences.org/articles/106545/elife-106545-mdarchecklist1-v1.docx
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Addressing cultural and knowledge barriers to enable preclinical sex inclusive research
eLife 14:RP106545.
https://doi.org/10.7554/eLife.106545.3
