Table A1 Clause Samples

Table A1. Exceptional changes to Table A (to be approved by e-mail or signature by the student, the responsible person in the Sending Institution and the responsible person in the Receiving Institution) Table B1: Exceptional changes to Table B (if applicable) (to be approved by e-mail or signature by the student and the responsible person in the Sending Institution) Component code (if any) Component title at the Receiving Institution (as indicated in the course catalogue) Component without changes [tick if applicable] Deleted component [tick if applicable] Added component [tick if applicable] Reason for changev Semester Number of ECTS credits (or equivalent) Component code (if any) Component title at the Sending Institution (as indicated in the course catalogue) Component without changes [tick if applicable] Deleted component [tick if applicable] Added component [tick if applicable] Semester Number of ECTS credits (or equivalent) Others: Total – in original LA (Table A): Total – in original LA (Table B): Total – deleted components: Total – deleted components: Total – added components: Total – added components: Total after changes: Total after changes: Responsible person at the Sending Institution Responsible person at the Receiving Institution i Course catalogue: detailed, user-friendly and up-to-date information on the institution’s learning environment that should be available to students before the mobility period and throughout their studies to enable them to make the right choices and use their time most efficiently. The information concerns, for example, the qualifications offered, the learning, teaching and assessment procedures, the level of programmes, the individual educational components and the learning resources. The Course Catalogue should include the names of people to contact, with information about how, when and where to contact them. Lines in the table can be added. Empty lines should be deleted or crossed out.

Table A1. 1 “Subset A of Licensed Patents”, Table A1.2 “Subset B of Licensed Patents”, and Table 1.3 “Background Patents” are hereby deleted in their entirety and replaced as follows: [* * *] [* * *] [* * *] [* * *] [* * *] [* * *] [* * *] [* * *] [* * *] [* * *] [* * *] [* * *]

Table A1. Results of a random-effects probit regression of the probability of success, round number (Round), treatments (FULL_ALL, STF, ST, FULL_SCRAMBLE, VEC_SCRABMLE), and interactions between round number and treatment (FULL_Round, STF_Round, ST_Round). Standard errors in brackets; * indicates significant at the 10% level,

Table A1. Mixed model analyses comparing the differences between the blended acceptance and commitment therapy and cognitive behavioral therapy group over time Outcome b SE t p Appendix 2 Supplementary material 1. Links to videos created for older adults ▇▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇.▇▇▇/watch?v=QCcDCvt9N5E ▇▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇.▇▇▇/watch?v=HlBp_5oUeMw ▇▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇.▇▇▇/watch?v=4zUTpkXTR1c ▇▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇.▇▇▇/watch?v=d4BMGtrHTi0 References 1. World Health Organization. Ageing and health. Updated October 4 2021. ▇▇▇▇▇://▇▇▇.▇▇▇. int/news-room/fact-sheets/detail/ageing-and-health 2. Centraal Bureau voor de Statistiek. Leeftijdsverdeling ▇▇▇ ziet de leeftijdsopbouw van de Nederlandse bevolking eruit? Updated June 9 2021. ▇▇▇▇▇://▇▇▇.▇▇▇.▇▇/nl-nl/visualisaties/ dashboard-bevolking/leeftijd/bevolking 3. World Health Organization. Mental health of older adults. Updated December 12 2017. ▇▇▇▇▇://▇▇▇.▇▇▇.▇▇▇/news-room/fact-sheets/detail/mental-health-of-older-adults

Table A1. Summary Statistics for Data Used for Econometric Results on Institutional Gaps and Income Gaps (Figures 4 and 7) Samplea Variable No. observations Mean Standard deviation Minimum Maximum Group 1 ICRG variablesb Log (country’s GDP per capita/USA GDP 414 923 –0.4069638 –1.715673 0.558766 0.579324 –1.75361 –3.65967 0.6972296 –0.3095284 per capita) Group 2 ICRG variablesb Log (country’s GDP per capita/USA GDP 162 378 –0.1312372 –1.328616 0.4356544 0.3673385 –1.00386 –2.19757 0.6972296 –0.3095284 per capita, PPP adjusted)

Table A1. Exceptional changes to Table A (to be approved by e-mail or signature by the student, the responsible person in the Sending Institution and the responsible person in the Receiving Institution) Table B1: Exceptional changes to Table B (if applicable) (to be approved by e-mail or signature by the student and the responsible person in the Sending Institution) Component code (if any) Component title at the Receiving Institution (as indicated in the course catalogue) Component without changes [tick if applicable] Deleted component [tick if applicable] Added component [tick if applicable] Reason for changev Semester Number of ECTS credits (or equivalent) Component code (if any) Component title at the Sending Institution (as indicated in the course catalogue) Component without changes [tick if applicable] Deleted component [tick if applicable] Added component [tick if applicable] Semester Number of ECTS credits (or equivalent) Others: Total – in original LA (Table A): Total – in original LA (Table B): Total – deleted components: Total – deleted components: Total – added components: Total – added components: Total after changes: Total after changes:

Table A1. Test for Parallel Pre-Policy Trends (1) (2) (3) (4) (5) (6) (7) VARIABLES Minutes Any New Any Lab Any Image # Visits in Specialty Any Health with PCP Medication Test Past 12mo Referral Education Comparison a. Owner vs. Non-Owner PCPs for Medicaid visits -0.227 -0.0138 -0.0217* 0.00653 -0.372** 0.000722 -0.0152 (0.638) (0.0423) (0.0154) (0.0328) (0.195) (0.0167) (0.0184) Interaction of linear semiannual time trend and dummy for owner PCPs Comparison b. Medicaid vs. Privately Insured Visits -0.162 0.0093 -0.0167 0.00258 -0.144 0.00325 0.0162 (1.033) (0.0218) (0.0844) (0.00952) (0.293) (0.0262) (0.0847) Interaction of linear semiannual time trend and dummy for owner PCPs Notes. The sample for comparison a) consists of 12,899 primary care visits made by Medicaid patients; the sample for comparison b) consists of 46,727 primary care visits made by Medicaid or privately insured patients. Interaction terms are between linear semiannual time trend and a dummy for treatment group. Linear semiannual time trend begins with the first half of 2010 being 0, the second half of 2010 being 1, the first half of 2012 being 2, and so on so forth. Estimates for control variables are omitted. Standard errors (in parentheses) are clustered at the physician level. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Appendix A2. Number of Primary Care Office Visits I examined some national and per-PCP indictors related to office visits. The purpose was to identify any change on the extensive margin of primary care supply. I calculated the indicators for owner and non-owner PCPs separately (Tables A2 and A3, respectively). First, I relied on NAMCS patient visit weights to estimate national aggregate numbers of office visits (item A). Secondly, I relied on physician weights to estimate number of owner/non-owner PCPs that saw the privately insured and Medicaid patients (item B). Note that number of PCPs seeing the privately insured was always higher than the number of PCPs seeing Medicaid patients. Thirdly, I estimated on average how many office visits a PCP offered to each type of patients, by dividing item A by item B (item C). Then, I cited the average number of office visits to a patient’s PCP in the past 12 months from Figure 2 (item D). Lastly, I divided item A by item D, to get an estimate of total number of patients who ever had an office visit (item E). I found that, overall, owner and non-owner PCPs offered less office visits to non-elderly adult Medicaid benefic...

Table A1. Probability of a polygon being assigned correctly within ±0, 1, 2, 3 ice concentration categories (user’s accuracy, or p(m|x)). pˆ refers to the point estimate of the likelihood found in our sample data; lower and upper refer to the lower and upper 95% confidence intervals. Ice ±0 ±1 ±2 ±3 Concentration pˆ Lower Upper pˆ Lower Upper pˆ Lower Upper pˆ Lower Upper All 0.386 0.339 0.435 0.838 0.798 0.871 0.937 0.908 0.957 0.990 0.974 0.996 3/10 0.517 0.344 0.686 0.966 0.828 0.998 0.966 0.828 0.998 1.000 0.883 1.000 4/10 0.341 0.219 0.489 0.864 0.733 0.836 1.000 0.920 1.000 1.000 0.920 1.000 5/10 0.370 0.254 0.504 0.796 0.671 0.882 0.907 0.801 0.960 1.000 0.934 1.000 6/10 0.423 0.299 0.558 0.827 0.703 0.906 0.904 0.794 0.958 1.000 0.931 1.000 7/10 0.389 0.285 0.504 0.847 0.747 0.912 0.944 0.866 0.978 0.986 0.925 0.999 8/10 0.513 0.404 0.621 0.859 0.765 0.919 0.949 0.875 0.980 1.000 0.953 1.000 9/10 0.185 0.109 0.296 0.769 0.654 0.855 0.908 0.813 0.957 0.954 0.873 0.954 ▇▇▇▇▇://▇▇▇.▇▇▇/10.5194/tc-2019-190 Preprint. Discussion started: 30 August 2019

Table A1. Orange-fleshed sweet potato varieities received in the full and partial intervention groups of the Quality Diets for Better Health study1 N Full Partial P-difference2 Alamura 68 37 (54.4) 31 (45.6) 0.16 Dilla 67 36 (53.7) 31 (46.3) 0.01 Halaba 53 34 (64.2) 19 (35.8) 0.40 Kabode 48 33 (68.8) 15 (31.3) 0.01 Kulfu 87 46 (52.9) 41 (47.1) 0.27 Naspot12 18 13 (72.2) 5 (27.8) 0.12 Naspot13 19 6 (31.6) 13 (68.4) 0.05 Tule 2 1 (50.0) 1 (50.0) NA Vita 23 13 (56.5) 10 (43.5) 0.53 Other 1 0 (0.0) 1 (100.0) NA 1 Values are in row percentages shown as n(%). Full, full intervention (agriculture activities + nutrition/health education + Healthy Baby Toolkit); Partial, partial intervention group (agriculture activities + nutrition/health education). OFSP, orange-fleshed sweet potatoes.