2016 AgrAbility National Training Meeting, Fort Collins, CO April 13, 2016 3:30-4:10 pm By Robert J. Fetsch, Extension Specialist & Professor Emeritus Co- Project Director, Colorado AgrAbility Project, Human Development & Family Studies Colorado State University & NAP Evaluation Committee AANTWMcGillQOL4.1316 (Rev. 4.0116) Pass out 40 copies of .ppt @ 6 slides per page.

To “McGill QOL and ILW Levels: Preliminary Experimental-Control Group Differences.”

AgrAbility Experimental-Control Group Differences in QOL and ILW Levels By Robert J. Fetsch (CSU), Chip Petrea (UIL), Robert Aherin (UIL), Sheila Simmons (KU), Vicki Janisch, Hannah Gerbitz, & Abigail Jensen (UW), Candy Leathers & Danielle Jackman (CSU/Goodwill Denver), Sharry Nielsen (UN), Rick Peterson (TAMU), Diana Sargent (OSU), Linda Fetzer (PSU), Toby Woodson (UAR), Leilani Carlson (UME), Inetta Fluharty (WVU), Kirk Ballin (ESVA), & Michele Proctor & Madeline McCauley (ECU). Thank you! Thanks to each of you for your contributions of experimental group data to this program evaluation study. And special thanks to Chip Petrea for your contributions of control group data to this program evaluation study. This study would not have been possible without the ongoing dedication and good work of our colleagues—Sheila Simmons, Bob Meyer, Vicki Janisch, Vince Luke, Rick Peterson, Toby Woodson, Kirk Ballin, Bob Aherin, Inetta Fluharty, Sharry Nielsen, Diana Sargent, and Tina Little. Could we have a hand of appreciation for them? Several years ago during the previous administration, President Bush asked all the Secretaries, including the Secretary of Agriculture (who is Brad Rein’s and our Superior), to assess their success at increasing Quality of Life Levels. In 2007? we told Brad Rein that we could help him assess AgrAbility’s success at increasing new clients’ QOL levels. He asked us to provide him with recommendations (by 8/1/09). We formed the National AA Evaluation Committee. Would Committee members present please wave? We looked at 100 QOL tools and picked 2—SF-36 and the McGill QOL. We pilot tested the McGill QOL over the past four years as a pre- and post-survey in 9 states to see if it works with farmers and ranchers and to see if we observe increases in QOL levels. Currently, President Obama is asking, “What works?” as he decides which programs to fund and which to cut. We in AR, CO, KS, ME, MN, MO, NC, NE, OH, OK, TX, UT, VA, WI, and WV are working as a team to answer two questions for Brad:

What’s our mission?

Our AgrAbility Mission “The AgrAbility Mission is to enhance and protect quality of life and preserve livelihoods. It’s about supporting and promoting growth and independence. Ultimately it’s about hope.” Source: National AgrAbility Project. (2011). It’s about hope [DVD]. Author: Purdue University. “The vision of AgrAbility is to enhance quality of life for farmers, ranchers, and other agricultural workers with disabilities.” Retrieved 3/30/16 from agrability.org . 5

How do we enhance our clients’ QOL?

How Do We Enhance Our Clients’ QOL? By connecting with them and their family stakeholders. By assisting them to reach their individual and family goals. By hearing and acknowledging what they really want.

Independent Living & Working Survey (ILW) I am able to… Complete chores on my farm/ranch. Operate machinery. Manage my farm/ranch. Access workspaces on my farm/ranch. Live in my home on the farm/ranch Change or modify my machinery in order to accommodate my needs. Here I wish to acknowledge Carla Wilhite and express NAP’s Evaluation Committee’s gratitude to Carla. Nine years ago (before 2/20/2007 which was the day we received the first QOL pretest) Carla and others insisted to the other 20 or so of us on the Evaluation Committee that we need to ask our clients about their abilities to complete chores, modify machinery, live in their homes on the ranch/farm both before and after they receive AgrAbility information, education, and service. Thank you, Carla!

We are fortunate to be recipients of USDA NIFA AgrAbility funding We are fortunate to be recipients of USDA NIFA AgrAbility funding. We are grateful for the opportunities to connect well with farm and ranch families with functional limitations.

“Good News” AgrAbility is among the 45 federally funded programs that supported employment for people with disabilities in fiscal year 2010. AgrAbility is among the 10/45 programs with a review or study to evaluate the program’s effectiveness. Source: U.S. Government Accounting Office. (2012). Employment for people with disabilities; Little is known about the effectiveness of fragmented and overlapping programs (GAO Publication No. 12-677). Washington, DC. (p. i). 10

“Good News” “…The Department of Agriculture’s AgrAbility program conducted a review of its activities between 1991 and 2011 and found that 11,000 clients had been served, and that 88 percent of those clients continued to be engaged in farm or ranch activities.” Source: U.S. Government Accounting Office. (2012). Employment for people with disabilities; Little is known about the effectiveness of fragmented and overlapping programs (GAO Publication No. 12-677). Washington, DC. (p. 27). Thanks to Bill Field, Paul Jones, Bob Aherin, Chip Petrea, and colleagues. 11

“Bad News” “However, this study did not determine whether other factors may have contributed to participants’ positive outcomes.” “No impact study.” Source: U.S. Government Accounting Office. (2012). Employment for people with disabilities; Little is known about the effectiveness of fragmented and overlapping programs (GAO Publication No. 12-677). Washington, DC. (pp. 27, 80). 12

How do we know these results are not due to something other than our AgrAbility information, education and service?

Aida Balsano & Brad Rein asked us to help respond. So far 16 SRAP’s are working to collect data from AgrAbility clients with an on-site visit (AR, CO, GA, KS, ME, MO, NC, NE, OH, OK, PA, TX, UT, VA, WI, & WV). AR, CO, GA, KS, ME, MO, NC, NE, OH, OK, PA, TX, UT, VA, WI, & WV ME = Maine MI = Michigan MN = Minnesota We invite you to join us, CA, IN, KY, MI, TN, & VT. 14

NAP CSUE UIUC Both UIUC and CSUE appreciate the funding for evaluation from NAP. NAP also will provide contact information on farmers and ranchers with disabilities from non-funded states who call their 1-800 telephone number to UIUC. You too can refer ranchers and farmers with disabilities who do not receive an on-site visit nor AgrAbility services to UIUC for the Control Group. UIUC will strive to get 40-50 pre-surveys/year and 100 matched pre- and post-surveys in 4 years for the Control Group and will e-mail the data to CSUE. We think we’ll need 40-50/year in order to end up with 100 matched pre-post surveys in the next 4 years. UIUC and CSUE will continue to work with 16+ SRAPs to collect matching pre-post surveys for the Experimental Group. So far we have 191 matched surveys in the Experimental group whose data we have analyzed as presented earlier today. Our goal is to collect matching pre-post surveys from 200 more new clients within the next 4 years. 15

What Our NAP Evaluation Committee Decided to Do Was… To compare two groups’ Pretest-Posttest QOL & ILW levels 200 Experimental Group participants who complete matched pretest- and posttest-surveys. 100 Control Group participants who complete matched pretest- and posttest-surveys. 16

Control Group (N = 100) Cannot be receiving any type of AgrAbility program services or onsite visits regardless of whether they are in USDA funded or Affiliate States. 17

Chip Petrea worked diligently with the No-Treatment Control Group. Chip provided us with 100 matched pretests and posttests. None of the Control Group participants ever received AgrAbility services currently or in the past. Thank you, Chip! 18

How Did Chip Petrea Recruit the Control Group? Made 1:1 requests of people he knew in states without currently funded AgrAbility projects and asked them for referrals. Requested referrals from national organizations serving agriculture, aging, and people with disabilities. Requested referrals from state Farm Bureaus.

How Did Chip Petrea Recruit the Control Group? Requested referrals from State Extension offices. Requested referrals from state colleges and regional community colleges with agricultural departments. Asked NAP and NAPEC for referrals from non-funded states.

History of NAPEC Who is an AgrAbility Client? An AgrAbility client is an individual with a disability engaged in production agriculture as an owner/operator, family member, or employee who has received professional services from AgrAbility project staff during an on-site visit.

Measures Used in 12-State Study McGill Quality of Life (QOL) Survey & AgrAbility Independent Living & Working Survey (ILW) NAP Demographic Data

History of NAPEC Twelve SRAP’s conducted a 9-year (2/20/2007-1/20/2016) experimental-control, pretest-posttest study to answer three questions: Do our clients improve their ILW & QOL levels? How much do they improve? Do they improve more than a no-treatment control group? 2/20/2007-1/20/2016 include the first entrya and the last exitb for the 225 included in the QOLExpCon study. Note: According to QOLMss6.1813, 6.5 years = 1/26/2007-6/18/2013. QOLILWMss., pp. 8.5, 10.2 N = 476/788 (QOLILWMss., p. 10.2).

History of NAPEC By January 20, 2016 12 States/SRAPs entered their 225 matched pre-post-survey data into Excel files and e-mailed them to CO for entering and analyzing. KS 82 36.4% WI 67 29.8% CO 27 12.0% NE 11 4.9% TX 9 4.0% OK 6 2.7% PA 6 2.7% AR 4 1.8% ME 4 1.8% WV 4 1.8% VA 3 1.3% NC 2 0.9% Total 225 100.0%

History of NAPEC By January 20, 2016 The Control Group consisted of 100 participants from 16 states plus NAP. IL 27 27% IA 17 17% TX 13 13% NY 7 7% MS 6 6% AL 5 5% FL 4 4% MO 4 4% AR 3 3% WY 3 3% MT 2 2% OR 2 2% MN 2 2% NAP 2 2% AZ 1 1% CA 1 1% WA 1 1% Total 100 100%

What Was the Average Length of Time with AgrAbility? The amount of time spent by experimental group participants with AgrAbility ranged from 1 to 74 months (M = 14.85; SD = 10.18; N = 225). The amount of time spent by control group participants ranged from 12 to 19 months (M = 13.76; SD = 0.98; N = 100).

So, how are we doing at enhancing our clients’ QOL levels and ILW levels?

AgrAbility Experimental Group QOL Pretest-Posttest Changes On average, our 201 AgrAbility clients began their QOL levels at 5.57 on a scale of 0-10 and ended their time with us at 7.15 (p<.001). Gain Score = 1.58 This is a 28.38% increase in QOL levels. These results are statistically significant. They are real, worthy of note. Note that if any of the items are missing for a case, SPSS will not compute a total score. These results are real, not by chance. They are statistically significant. They need to be taken seriously. For those of you who are interested in more detail, there’s a language, a way of thinking, a set of conventions that researchers use that I can go into more detail later with those who are interested. P < .05 means that these results are real, are statistically significant, and are due to chance < 5 times out of 100. *indicates p <.05. P < .01 means that these results are statistically significant and are due to chance < 1 time out of 100. ** indicates p < .01. P < .001 means that these results are statistically significant and are due to chance < 1 time out of 1,000. *** indicates p < .001. (R.K. Yang, personal communication, September 15, 2009). 28

McGill Pretest-Posttest Changes Control Group McGill Pretest-Posttest Changes On average, our 100 no-treatment control group participants began their QOL levels at 5.09 on a scale of 0-10 and actually decreased to 4.91 in a little over 13 months (N.S.). These results are not statistically significant. Loss score = -.18 This is a 3.5% decrease in QOL levels. These results are statistically significant. They are real, worthy of note. Note that if any of the items are missing for a case, SPSS will not compute a total score. These results are real, not by chance. They are statistically significant. They need to be taken seriously. For those of you who are interested in more detail, there’s a language, a way of thinking, a set of conventions that researchers use that I can go into more detail later with those who are interested. P < .05 means that these results are real, are statistically significant, and are due to chance < 5 times out of 100. *indicates p <.05. P < .01 means that these results are statistically significant and are due to chance < 1 time out of 100. ** indicates p < .01. P < .001 means that these results are statistically significant and are due to chance < 1 time out of 1,000. *** indicates p < .001. (R.K. Yang, personal communication, September 15, 2009). 29

McGill QOL Pretest-Posttest Total Score Changes for Experimental and Control Groups Life-threatening experience—How many of us have experienced it either directly or via someone we care deeply about? In my experience and in the experience of many of my farm/ranch friends, when we face our own mortality, we raise deep questions about who we are and what life is all about. How many of us have experienced this too? Existential ~ Experiential. 4/27/15 Support (N=188)*** M=6.197.60 As of 4/27/15, the effect size was .72 which is large or larger than typical (Cohen, 1988; Morgan et al., 2004, p. 91; Morgan et al., 2011, p. 101.3) Existential/Experiential Well-Being (N=190)*** M=6.117.47 As of 4/27/15, the effect size was .74 which is large or larger than typical (Cohen, 1988; Morgan et al., 2004, p. 91; Morgan et al., 2011, p. 101.3) Psychological Well-Being (N=187)*** M=5.787.27 As of 4/27/15, the effect size was .66 which is large or larger than typical (Cohen, 1988; Morgan et al., 2004, p. 91; Morgan et al., 2011, p. 101.3) Note again—AgrAbility participants’ Quality of Life subscale scores rose from Pre-Survey to Post-Survey at a statistically significant amount. These results are real and are due to chance < 1 time out of 1,000. Experiential, existential, i.e. derived from experience or the experience of existence. Synonyms: experiential, existential Antonyms: theoretic, theoretical Retrieved September 4, 2011 from http://www.synonyms.net/synonym/existential 30

AgrAbility Experimental Group ILW Changes On average our 200 AgrAbility clients began their ILW levels at 17.00 and increased to 21.91. Their gain score = 4.91. This is a 28.88% increase in ILW levels. Finally, on the ILW Total Score, AgrAbility participants’ total score rose from Pre-Survey to Post Survey. These results are statistically significant. They are real, worthy of note. Note that if any of the items are missing for a case, SPSS will not compute a total score. These results are real, not by chance. They are statistically significant. They need to be taken seriously. For those of you who are interested in more detail, there’s a language, a way of thinking, a set of conventions that researchers use that I can go into more detail later with those who are interested. P < .05 means that these results are real, are statistically significant, and are due to chance < 5 times out of 100. *indicates p <.05. P < .01 means that these results are statistically significant and are due to chance < 1 time out of 100. ** indicates p < .01. P < .001 means that these results are statistically significant and are due to chance < 1 time out of 1,000. *** indicates p < .001. (R.K. Yang, personal communication, September 15, 2009). 31

Control Group ILW Changes On average our 100 no-treatment control group participants began with an ILW level of 19.42 and ended at 21.02. Gain score = 1.6. This is an 8.2% increase in ILW levels. Finally, on the ILW Total Score, AgrAbility participants’ total score rose from Pre-Survey to Post Survey. These results are statistically significant. They are real, worthy of note. Note that if any of the items are missing for a case, SPSS will not compute a total score. These results are real, not by chance. They are statistically significant. They need to be taken seriously. For those of you who are interested in more detail, there’s a language, a way of thinking, a set of conventions that researchers use that I can go into more detail later with those who are interested. P < .05 means that these results are real, are statistically significant, and are due to chance < 5 times out of 100. *indicates p <.05. P < .01 means that these results are statistically significant and are due to chance < 1 time out of 100. ** indicates p < .01. P < .001 means that these results are statistically significant and are due to chance < 1 time out of 1,000. *** indicates p < .001. (R.K. Yang, personal communication, September 15, 2009). 32

ILW Pretest-Posttest Total Score Changes for Experimental and Control Groups 4/27/15 ILW Total Score (N=168)*** M=16.9521.80 As of 4/27/15, the effect size is .84 which is large or larger than typical (Cohen, 1988; Morgan et al., 2004, p. 91; Morgan et al., 2011, p. 101.3) Finally, on the ILW Total Score, AgrAbility participants’ QOL total score rose from Pre-Survey to Post Survey. These results are statistically significant. They are real, worthy of note. Note that if any of the items are missing for a case, SPSS will not compute a total score. These results are real, not by chance. They are statistically significant. They need to be taken seriously. For those of you who are interested in more detail, there’s a language, a way of thinking, a set of conventions that researchers use that I can go into more detail later with those who are interested. P < .05 means that these results are real, are statistically significant, and are due to chance < 5 times out of 100. *indicates p <.05. P < .01 means that these results are statistically significant and are due to chance < 1 time out of 100. ** indicates p < .01. P < .001 means that these results are statistically significant and are due to chance < 1 time out of 1,000. *** indicates p < .001. (R.K. Yang, personal communication, September 15, 2009). 33

One of the better ways to compare empirical pretest-posttest changes of an experimental group with a no-treatment control group like ours is to calculate change scores.

MQOL Gain Scores for Experimental & Control Groups Our experimental group 7.15 -5.57 = 1.58 Gain score. Our control group 4.91 -5.09 = -.18 Gain score. Gain score***; d = 1.11 (Large). The two groups’ Gain Scores were statistically significantly different (p < .001). Our colleague, Bob Aherin, during our most recent NAPEC teleconference on 3/8/16, when he first saw these impressive results, asked me, “What is the biggest surprise you found?” The biggest surprise I found was the large increase in our clients’ QOL levels from the time they began their work with us to the time they finished their work with us. I think now would be a good time for us to give a big hand to the 16 SRAPs involved in this QOL study, don’t you? And I think now would be a good time for us to give a big hand to our colleague who did all the work to collect the control group data—Chip Petrea….

ILW Gain Scores for Experimental & Control Groups Our experimental group 21.91 -17.00 = 4.91. Our control group 21.02 -19.42 = 1.60. Gain score ***; d = .5 (Medium). The two groups’ gain scores were different (p<.001).

These gain scores suggest that AgrAbility is more effective at increasing QOL and ILW levels than a no-treatment control group.

81.9% of Experimental Group Improved Their 7.15 As of 3/29/16 the Experimental Group Pre 5.577.15 (N = 199): -81.9% (163/199) improved on QOL; -18.1% (36/199) decreased on QOL; and -0% (0/199) remained the same on QOL. The Control Group Pre 5.09Post 4.91 (N = 97): -44.3% (43/97) improved on QOL: -54.6% (53/97) decreased on QOL; and -1.0% (1/97) remained the same on QOL. Fetsch, R. J., Jackman, D. M., & Collins, C. L. (2015, July 23). Pretest-posttest quality of life and independent living and working levels among farmers and ranchers with disabilities. Manuscript in preparation. Psychological Well-Being Pretest = 5.78 (SD = 2.41; N = 187) & Posttest = 7.27 (SD = 2.10; N = 187), t = -8.80, df = 186, p = .000, p < .001, d = .66, which is large or larger than typical (Cohen, 1988). (Cf. p. 40.7.) Cohen, J. (1988). Statistical power and analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. 81.9% of Experimental Group Improved Their Quality of Life Levels (Range = 0-10) 5.57

83.0% of Experimental Group Improved Their Independent 21.91 As of 3/29/16 the Experimental Group Pre 17.0021.91 (N = 200): -83% (166/200) improved on ILW; -12% (24/200) decreased on ILW; and -5% (10/200) remained the same on ILW. The Control Group Pre 19.42Post 21.02 (N = 100): -58% (58/100) improved on ILW: -38% (38/100) decreased on ILW; and -4% (4/100) remained the same on ILW. Fetsch, R. J., Jackman, D. M., & Collins, C. L. (2015, July 23). Pretest-posttest quality of life and independent living and working levels among farmers and ranchers with disabilities. Manuscript in preparation. Psychological Well-Being Pretest = 5.78 (SD = 2.41; N = 187) & Posttest = 7.27 (SD = 2.10; N = 187), t = -8.80, df = 186, p = .000, p < .001, d = .66, which is large or larger than typical (Cohen, 1988). (Cf. p. 40.7.) Cohen, J. (1988). Statistical power and analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. 83.0% of Experimental Group Improved Their Independent Living and Working Levels (Range = 0-30) 17.00

Percent of Both Groups that Increased, Decreased, and Made No Change in QOL from Pretest to Posttest As of 3/29/16 the Experimental Group Pre 5.577.15 (N = 199): -81.9% (163/199) improved on QOL; -18.1% (36/199) decreased on QOL; and -0% (0/199) remained the same on QOL. The Control Group Pre 5.09Post 4.91 (N = 97): -44.3% (43/97) improved on QOL: -54.6% (53/97) decreased on QOL; and -1.0% (1/97) remained the same on QOL.

Percent of Both Groups that Increased, Decreased, and Made No Change in ILW from Pretest to Posttest As of 3/29/16 the Experimental Group Pre 17.0021.91 (N = 200): -83% (166/200) improved on ILW; -12% (24/200) decreased on ILW; and -5% (10/200) remained the same on ILW. The Control Group Pre 19.42Post 21.02 (N = 100): -58% (58/100) improved on ILW: -38% (38/100) decreased on ILW; and -4% (4/100) remained the same on ILW.

These matched pretest-posttest results suggest that 12 AgrAbility Projects are more effective than a no-treatment control group at increasing QOL and ILW levels (AR, CO, KS, ME, NC, NE, OK, PA, TX, VA, WI, & WV) and IL with the control group.

These preliminary results suggest that 12 SRAPs are making a difference in the lives of their clients that is for the better. I continue to work with two consultants to be sure that the results we report are the most accurate and complete that we can provide.

We now have data from a no-treatment control group. “Good News” We now have data from a no-treatment control group. We now have empirical evidence that suggests that the increases in QOL and ILW are due to AgrAbility in 12 states/SRAPs. 44

We are building a road to Evidence-Based AgrAbility Programming over the next four years—Together!

Let’s brainstorm some implications and practical steps that AgrAbility teams can take.

Please Join Us! Experimental SRAPs Currently unfunded Control SRAPs 10/14/2015 20 SRAPS/20 States are currently funded (CA, CO, GA, IL, IN, KS, KY, ME, MI, MO, NC, NE, OH, PA, TN, TX, VA, UT, WI, and WV). Of these 20, 14 (70%) are using the QOL and ILW (CO, GA, KS, ME, MO, NC, NE, OH, PA, TX, UT, VA, WI, & WV) plus 2 previously funded SRAPS (AR & OK) = 16 SRAPS are using QOL & ILW. (AgrAbility Green) 16 Experimental Group States (AR, CO, GA, KS, ME, MO, NC, NE, OH, OK, PA, TX, UT, VA, WI, & WV (16 SRAPS). (Purple) 16 Control Group States + NAP (AL, AR, AZ, CA, FL, IA, IL, MN, MO, MT, MS, NAP, NY, OR, TX, WA, WY) 6 currently funded SRAPs are not yet with us using the QOL & ILW (Red)—Won’t You Join Us? (CA, IL, IN, KY, MI, & TN). 7 SRAPs are formerly funded SRAPs (Blue) (IA, LA, MN, MS, NH, VT, & WY). 2 SRAPs are newly funded as of 10/14/15 (IL & MI). -Click on the State to change. -Right click Format Shape. -FillSolid FillColor -Olive Green, Accent 3? To change color to AgrAbility Green, use More Colors, Color Model RGB, Red = 118, Green = 202, Blue = 94, Transparency = 0%. -Close -Blue is in Standard Colors, 3rd from the right. -Purple is in Standard Colors, 1st from the right. -Red is in Standard Colors, 2nd from the left. -Yellow is in Standard Colors, 4th from the left. Please Join Us! Experimental SRAPs

How many SRAPs are collecting QOL and ILW data from their new clients? 3/19/16 AR, CO, GA, KS, ME, MO, NC, NE, OH, OK, PA, TX, UT, VA, WI, & WV (16 States) To update this slide: -Right click on left axis. -Click on Edit data. -Enter new number(s). -Close Sheet 2 by clicking on the X in the upper right corner. How many SRAPs are collecting QOL and ILW data from their new clients? 49

Why Join Us? Document your project’s effectiveness at increasing clients’ ILW and QOL levels. Enhance your chances of receiving funding next time with empirical evidence of your SRAP’s quality and effectiveness. Increase your chances for outside funding by demonstrating your accountability. Contribute to AgrAbility’s Mission.

Won’t You Join Us? Here’s how: Send an email to robert.fetsch@colostate.edu. Seek IRB approval from your Land-Grant University. Study and use the same protocol. Adapt CO to __ on pp. 1-2 & mail. Enter your data into an Excel file that we will provide, proof perfectly & email to me.

Who Were the Participants in the Groups (EN = 225; CN = 100)? Gender Experimental Control Male 67.1% 75% Female 27.1% 25% N.R. 5.8% 0% Role on the farm Operators/Owners 66.7% 54% Spouses/Partners 14.7% 33% Work status Full-Time 58.7% 25% Part-Time 16.9% 32% Occasional 12.0% 38% QOLILWMss., p. 8.4 and 8.6 3/30/15: Education level was reported by 24/191 = 12.6%. Ethnicity was reported by 11/191 = 5.8%. Total household income level was reported by 11/191 = 5.8% No. days off-farm was reported by 11/191 = 5.8%. Veteran status was reported by 11/191 = 5.8%.

Who Were the Participants in the Groups (EN = 225; CN = 100)? Age Experimental Control M = 60.16 54.64 SD = 14.89 12.99 N = 199 99 Range = 20-95 19-80 *M Age in U.S. was 58.3. Source: Retrieved March 30, 2016 from http://www.agcensus.usda.gov/Publications/2012/Online_Resources/Highlights/Farm_Demographics/#average_age QOLILWMss., p. 8.4 and 8.6 3/30/15: Education level was reported by 24/191 = 12.6%. Ethnicity was reported by 11/191 = 5.8%. Total household income level was reported by 11/191 = 5.8% No. days off-farm was reported by 11/191 = 5.8%. Veteran status was reported by 11/191 = 5.8%.

Who Were the Participants in the Groups (EN = 40/225 or 17 Who Were the Participants in the Groups (EN = 40/225 or 17.8%; CN = 78/100 or 78%)? Education level Experimental Control Less than high school 0.4% 12% High school graduate/GED 11.1% 27% Some college/Technical school 3.6% 20% College graduate/More 2.7% 19% Missing 82.2% 22% QOLILWMss., p. 8.4 and 8.6 3/30/15: Education level was reported by 24/191 = 12.6%. Ethnicity was reported by 11/191 = 5.8%. Total household income level was reported by 11/191 = 5.8% No. days off-farm was reported by 11/191 = 5.8%. Veteran status was reported by 11/191 = 5.8%.

Who Were the Participants in the Groups (EN = 22/225 or 9 Who Were the Participants in the Groups (EN = 22/225 or 9.8%; CN = 78/100 or 78%)? Ethnicity Experimental Control White 9.3% 55% Black 13% American Indian/Alaska Native 2% Asian 3% Hispanic/Latino 0.4% 10% Other 1% Missing 90.2% 16% QOLILWMss., p. 8.4 and 8.6 3/30/15: Education level was reported by 24/191 = 12.6%. Ethnicity was reported by 11/191 = 5.8%. Total household income level was reported by 11/191 = 5.8% No. days off-farm was reported by 11/191 = 5.8%. Veteran status was reported by 11/191 = 5.8%.

Who Were the Participants in the Groups (EN = 22/225 or 9 Who Were the Participants in the Groups (EN = 22/225 or 9.8%; CN = 82/100 or 82%)? Total household income Experimental Control $60,000 or less 1.3% 34% $60,001-$120,000 6.7% 29% $120,001-$180,000 1.8% 13% $180,001-$240,000 6% Missing 90.2% 18% Veteran Yes 3.1% 22% No 6.7% 60% QOLILWMss., p. 8.4 and 8.6 3/30/15: Education level was reported by 24/191 = 12.6%. Ethnicity was reported by 11/191 = 5.8%. Total household income level was reported by 11/191 = 5.8% No. days off-farm was reported by 11/191 = 5.8%. Veteran status was reported by 11/191 = 5.8%.

Who Were the Participants in the Groups (EN = 22/225 or 9 Who Were the Participants in the Groups (EN = 22/225 or 9.8%; CN = 79/100 or 79%)? Number of days off farm Experimental Control None 3.1% 29% 1-49 days 6.7% 27% 50-99 days 13% 100-199 days 6% 200 days or more 4% Missing 90.2% 21% QOLILWMss., p. 8.4 and 8.6 3/30/15: Education level was reported by 24/191 = 12.6%. Ethnicity was reported by 11/191 = 5.8%. Total household income level was reported by 11/191 = 5.8% No. days off-farm was reported by 11/191 = 5.8%. Veteran status was reported by 11/191 = 5.8%.

Who Were the Participants in Both Groups? (EN = 225; CN = 100) Primary Agricultural Operation Experimental Control Field/grain 79 35.1% 11 11% Livestock 56 24.9% 39 39% Dairy 40 17.8% 22 22% Agribusiness 16 7.1% 5 5% Hay 6 2.7% 5 5% Vegetable 5 2.2% 4 4% Poultry 3 1.3% 1 1% Other Animal 3 1.3% 9 9% Other 5 2.2% 3 3% Missing 12 5.3% 1 1% QOLILWMss., p. 8.8

What Were Participants’ Primary Disabilities (EN = 225; CN = 100)? Arthritis 25 11.1% 17 17% Back injury 22 9.8% 8 8% Visual impairment 21 9.3% 3 3% Joint injury 20 8.9% 12 12% Spinal paraplegia & quadriplegia 14 6.2% 5 5% Orthopedic injury 13 5.8% 8 8% Stroke 11 4.9% 5 5% Cardiovascular disease 9 4.0% 1 1% Other 75 33.3% 41 41% Missing 15 6.7% 0 0% “Other” includes Above elbow amp., Below elbow amp., Hand amp., Finger amp., and Thumb amp. (Frequency = < 7?)

What Were Participants’ First Symptoms (EN = 225; CN = 100)? Pain 26 11.6% 3 3% Vision issues 22 9.8% 1 1% Back pain 17 7.6% 3 3% Walking 16 7.1% 1 1% Mobility 15 6.7% 0 0% Tiredness 12 5.3% 1 1% Immobility 11 4.9% 1 1% Hearing issues 8 3.6% 1 1% Sleep issues 7 3.1% 3 3% Other 91 40.4% 86 86% Missing 0 0.0% 0 0%

What Were Participants’ First Symptoms (EN = 225; CN = 100)? Pain 26 11.6% Digestive probs. 6 6% Vision issues 22 9.8% Kidney probs. 4 4% Back pain 17 7.6% Weight gain 4 4% Walking 16 7.1% Back pain 3 3% Mobility 15 6.7% Burns 3 3% Tiredness 12 5.3% Chest pain 3 3% Immobility 11 4.9% Diarrhea 3 3% Hearing issues 8 3.6% Eye pain 3 3% Sleep issues 7 3.1% Neck pain 3 3% Other 91 40.4% Pain 3 3% Missing 0 0.0% Sleep issues 3 3%

Increased Greatly/ Somewhat Neither Reduced Greatly/ What impact has your condition had on your farm/ranch’s… Increased Greatly/ Somewhat Neither Reduced Greatly/ Productivity? (N=143/225) 36% 26% 39% Financial return? (N=145/225) 23% 43% 35% 3/30/16 (For our Mss. with Aherin, see 9/21/11 Notes in National AA Evaluation Committee discussion with Sheila Simmons and Bob Aherin.) 62

“I would say because of my involvement with AgrAbility that our household income has improved.” Only 41/225 (18.2%) reported. Of those 41: 11 (4.9%) Strongly Agree 17 (7.6%) Agree 7 (3.1%) Neither Agree Nor Disagree 3 (1.3%) Disagree 3/30/16 (for Aida Balsano)

AgrAbility provided me with info/recommendations I used: Yes No To do my farm/ranch work better or more easily than before working with AgrAbility. (N=170/225) 83% 17% To continue my farming/ranching operation in part/whole, without help I would not have been able to do so. (N=165/225) 70% 30% To continue to live in my home independently. (N=165/225) 46% 54% To continue to live on the farm/ranch, but successfully take up another occupation. (N=157/225) 6% 94% AgrAbility did not provide me with help. (N=156/225) 9% 91% 3/30/16 Percentages don’t add to 100% because we told them on the survey item, “Check all that apply.” Maybe modify “To continue to live in my home independently” to “To continue to live in my home independently with assistance/without assistance?” [Seems to me that both options have problems. Do I really want to change this item now with only a couple of years to go?”] 64

I have received assistance… SA/A Neither D/SD NA To modify my farm/ranch structures. (N=145/225) 64% 11% 26% 0% With equipment modifications. (N=144/225) 12% 24% In determining profitability. (N=144/225) 34% 32% 3/30/16 (For our Mss. with Aherin, see 9/21/11 Notes in National AA Evaluation Committee discussion with Sheila Simmons and Bob Aherin.) 65

I… SA/A Neither D/SD NA Have received assistance/information that was useful in my farming operation. (N=210/225) 84% 6% 3% Would say because of my involvement with AgrAbility that our household income has improved. (N=41/225) 68% 17% 10% 5% Was able to follow through on the recommendations made by AgrAbility. (N=206/225) 79% 8% 3/30/16 (for Aida Balsano) (For our Mss. with Aherin, see 9/21/11 Notes in National AA Evaluation Committee discussion with Sheila Simmons and Bob Aherin.) 66

How Reliable Are the Subscales? A common measure of reliability is Cronbach’s alpha. Subscale (N = 325) Pre Post Physical Symptoms .50 .73 Psychological WB .82 .87 Existential WB .86 .90 Support .76 .74 MQOL Total .70 .84 ILW Total .74 .74 Updated as of 4/17/15. “Alphas should be positive and usually greater than .70 to provide good support for internal consistency reliability.” (Morgan, G. A., Leech, N. L., Gloeckner, G. W., & Barrett, K. C. (2004). SPSS for introductory statistics: Use and interpretation (2nd ed.). Mahwah, NY: Lawrence Erlbaum Associates. (p. 122.7). Sometimes 3-item subscales have lower Cronbach’s alphas. Ours is .59 which is marginal because it is (slightly) less than .70 (Morgan, Leech, Gloeckner, & Barrett, 2004, p. 124.2). The three-item physical symptoms subscale has at times had low Cronbach alphas in other studies ranging from .62 (Cohen et al., 1997) to .70 (Cohen, Mount, Strobel & Bui, 1995). The other 9 Cronbach alphas are .79-.93 which are quite acceptable because they are >.70. Cohen, R. S., Mount, B. M., Bruera, E., Provost, M., Rowe, J., & Tong, K. (1997). Validity of the McGill Quality of Life Questionnaire in the palliative care setting: A multi-centre Canadian study demonstrating the importance of the existential domain. Palliative Medicine, 11, 3-20. Cohen, R. S., Mount, B. M., Strobel, M. G., & Bui, F. (1995). The McGill Quality of Life Questionnaire: A measure of quality of life appropriate for people with advanced disease. A preliminary study of validity and acceptability. Palliative Medicine, 9, 207-219.

National AgrAbility Project Evaluation Committee (NAPEC) Produced Results Published 3 refereed journal articles; a 4th is “in press.” Christen, C. T., & Fetsch, R. J. (2008). Colorado AgrAbility: Enhancing the effectiveness of outreach efforts targeting farmers and ranchers with disabilities. Journal of Applied Communication, 92(1&2), 1-12. Fetsch, R. J., & Jackman, D. M. (2015, December). Colorado’s AgrAbility Project’s effects in KASA and practice changes with agricultural producers and professionals. Journal of Extension, 53(6), Number 6, Article # 6FEA6. Available from http://www.joe.org/joe/2015december/a6.php

NAPEC Produced Results Published 3 refereed journal articles; a 4th is “in press.” Jackman, D. M., Fetsch, R. J., & Collins, C. L. (in press). Quality of life and independent living and working levels of farmers and ranchers with disabilities. Disability and Health Journal, doi:10.1016/j.dhjo.2015.09.002 Meyer, R. H., & Fetsch, R. J. (2006). National AgrAbility Project impact on farmers and ranchers with disabilities. Journal of Agricultural Safety and Health, 12(4), 275-291.

Questions? & Answers

Thank you very much! Now let’s go back to the Major Results of this study. Slide #21 McGill Total Score Changes Improve from 5.65 to 7.12***. Slide #27 AAILW Total Score Changes Improve from 16.95 to 21.80***. Now I want to hear from you. Let’s brainstorm implications and practical steps that AgrAbility teams can take.

“Bad News” “However, this study did not determine whether other factors may have contributed to participants’ positive outcomes.” “No impact study.” Source: U.S. Government Accounting Office. (2012). Employment for people with disabilities; Little is known about the effectiveness of fragmented and overlapping programs (GAO Publication No. 12-677). Washington, DC. (pp. 27, 80). 72

How do we know these results are not due to something other than our AgrAbility information, education and service?