1 00:00:00,040 --> 00:00:02,200 Speaker 1: We got a medical insight. Three decades worth of a 2 00:00:02,279 --> 00:00:05,800 Speaker 1: Tigo University med school enrollment DART has been analyzed put 3 00:00:05,800 --> 00:00:09,120 Speaker 1: into a new report. The schemes aimed at boosting underrepresented communities, 4 00:00:09,320 --> 00:00:11,080 Speaker 1: they've had some impact. So in other words, to put 5 00:00:11,080 --> 00:00:12,559 Speaker 1: it really simply, what happens is you've got to get 6 00:00:12,560 --> 00:00:14,400 Speaker 1: a certain score to get into med school. If you're 7 00:00:14,480 --> 00:00:17,200 Speaker 1: MARI or various other backgrounds, you can get a lower score. 8 00:00:17,480 --> 00:00:21,120 Speaker 1: Students with wealthy backgrounds continue to fill the cohorts. Governments 9 00:00:21,120 --> 00:00:23,880 Speaker 1: reviewing the Schemes Act, of course, causes all discriminatory The 10 00:00:23,880 --> 00:00:27,319 Speaker 1: Auckland University Medical Emeritors Professor DEAs Gorman is well us 11 00:00:27,480 --> 00:00:28,440 Speaker 1: is very good morning to you. 12 00:00:29,560 --> 00:00:30,160 Speaker 2: Good mining Mike. 13 00:00:30,280 --> 00:00:32,440 Speaker 1: Do you reckon it's worked in any way, shape or 14 00:00:32,479 --> 00:00:33,800 Speaker 1: form that we're all happy with or not? 15 00:00:35,120 --> 00:00:37,520 Speaker 2: Well, it certainly worked in terms of increasing a number 16 00:00:37,600 --> 00:00:41,280 Speaker 2: of MARI doctors we have, but that's not the problem 17 00:00:41,280 --> 00:00:44,640 Speaker 2: that was designed to fix. Might The problem with designed 18 00:00:44,640 --> 00:00:47,920 Speaker 2: to fix was to improve access from my way to 19 00:00:48,000 --> 00:00:51,479 Speaker 2: healthcare and to improve their health outcomes. And it was 20 00:00:51,520 --> 00:00:55,240 Speaker 2: introduced more than fifty years ago. I went first to 21 00:00:55,600 --> 00:00:58,279 Speaker 2: score fifty three years ago. It was in place then 22 00:00:58,360 --> 00:01:02,520 Speaker 2: I didn't know that then. But the problem is designed 23 00:01:02,520 --> 00:01:05,160 Speaker 2: to fix us, not to have more brown places in 24 00:01:05,200 --> 00:01:08,040 Speaker 2: the medical queue. But it was actually to improve access 25 00:01:08,160 --> 00:01:11,360 Speaker 2: from our healthcare and improve our health outcomes, which it 26 00:01:11,480 --> 00:01:14,080 Speaker 2: is not. That's never been measured. 27 00:01:14,360 --> 00:01:16,080 Speaker 1: So the idea is, and this is the part I've 28 00:01:16,080 --> 00:01:18,200 Speaker 1: never understood. The idea is that if you have when 29 00:01:18,240 --> 00:01:20,200 Speaker 1: you turn up to the doctor and you're Maori and 30 00:01:20,240 --> 00:01:23,360 Speaker 1: the doctors Mari, somehow things change or things are different. 31 00:01:23,840 --> 00:01:24,560 Speaker 1: Is that true? 32 00:01:26,280 --> 00:01:28,120 Speaker 2: I don't think it is. I think we've had an 33 00:01:28,120 --> 00:01:31,560 Speaker 2: obligation on us as medical schools to prepare people who 34 00:01:31,640 --> 00:01:35,240 Speaker 2: are culturally confident from the get go, and that's never changed. 35 00:01:35,760 --> 00:01:38,760 Speaker 2: The idea. Also think that I said, if you're Maray 36 00:01:38,760 --> 00:01:40,840 Speaker 2: and more likely to go and work in a area 37 00:01:40,880 --> 00:01:43,959 Speaker 2: with an large number of Mari patients that is currently 38 00:01:44,080 --> 00:01:48,360 Speaker 2: under service, and in fact you take a kid a 39 00:01:48,480 --> 00:01:52,200 Speaker 2: rural town in terms of medical school in Auckland, I'd 40 00:01:52,200 --> 00:01:54,280 Speaker 2: suggest that that rural sounds the last nas you want 41 00:01:54,280 --> 00:01:54,720 Speaker 2: to go back to you. 42 00:01:54,920 --> 00:01:56,800 Speaker 1: Well, that's true, and we know that from these statistics 43 00:01:56,800 --> 00:01:59,800 Speaker 1: stu't we And it's also expanded, so it's Mari indigenous specific, 44 00:01:59,800 --> 00:02:03,440 Speaker 1: it's rural is refugee at students from lower socioeconomic households. 45 00:02:03,760 --> 00:02:08,120 Speaker 1: The lower socioeconomic thing hasn't moved the needle at all. 46 00:02:08,240 --> 00:02:10,640 Speaker 1: Are any of these things a problem and do they 47 00:02:10,720 --> 00:02:13,239 Speaker 1: need fixing or as long as we have enough people 48 00:02:13,600 --> 00:02:16,400 Speaker 1: becoming doctors, we've solved our problem. 49 00:02:17,520 --> 00:02:20,360 Speaker 2: Well. Look, I think NEWSLD is like the idea of 50 00:02:20,360 --> 00:02:23,040 Speaker 2: a meritocracy, like and we like the idea that the 51 00:02:23,080 --> 00:02:26,280 Speaker 2: best students gives to the medical school, and so a 52 00:02:26,400 --> 00:02:30,560 Speaker 2: process which distorts it in meritocracy will always have trouble with. 53 00:02:30,639 --> 00:02:32,560 Speaker 2: And I can tell you now that some of the 54 00:02:32,560 --> 00:02:35,840 Speaker 2: thirdest days of my time is it in medical school 55 00:02:35,960 --> 00:02:38,720 Speaker 2: was talking to families who were so distressed that there's 56 00:02:38,760 --> 00:02:42,240 Speaker 2: someone order didn't get into medicine. Because they did, they 57 00:02:42,240 --> 00:02:44,799 Speaker 2: were into a prefect group. Yeah. So I think we've 58 00:02:44,800 --> 00:02:48,320 Speaker 2: going to be very careful before we start fiddling with meritocracies, 59 00:02:48,680 --> 00:02:54,360 Speaker 2: and particularly if we start introducing social engineering without appropriate research. Yeah. 60 00:02:54,680 --> 00:02:56,320 Speaker 1: I tend to agree. What about the difference, and this 61 00:02:56,400 --> 00:02:59,000 Speaker 1: is outside, but the difference between Auckland and I target, 62 00:02:59,040 --> 00:03:01,800 Speaker 1: for example, is based on numbers. If you've got good numbers, 63 00:03:01,800 --> 00:03:04,000 Speaker 1: you get in in Auckland. It's it's got more to 64 00:03:04,040 --> 00:03:06,120 Speaker 1: do with the person they interview you, do you have 65 00:03:06,160 --> 00:03:06,960 Speaker 1: a view on that or not? 66 00:03:07,880 --> 00:03:11,040 Speaker 2: Oh, look, I don't think the interview contributes much to 67 00:03:11,120 --> 00:03:13,560 Speaker 2: the overall waiting. And if you look at the list 68 00:03:13,600 --> 00:03:16,320 Speaker 2: that would accept at Auckland, whether without the interview, it's 69 00:03:16,320 --> 00:03:17,119 Speaker 2: pretty much. 70 00:03:17,240 --> 00:03:18,000 Speaker 1: The same time. 71 00:03:18,120 --> 00:03:20,120 Speaker 2: Yeah, the lists be obviously you've got a bunch of 72 00:03:20,160 --> 00:03:24,239 Speaker 2: overachieving kids fill ducts and so on, or competing for 73 00:03:24,280 --> 00:03:27,480 Speaker 2: a limited number of ices. Mean you're telling it some 74 00:03:27,639 --> 00:03:29,760 Speaker 2: of those places are no longer available to them, so 75 00:03:30,560 --> 00:03:32,480 Speaker 2: will generate an ang still, of course it will. Then 76 00:03:33,080 --> 00:03:35,160 Speaker 2: how do you justify it? You're justify it on the 77 00:03:35,200 --> 00:03:38,360 Speaker 2: base of that you're improving overall community well being, But 78 00:03:38,440 --> 00:03:40,120 Speaker 2: if you haven't met it the how can you claim it? 79 00:03:40,520 --> 00:03:42,400 Speaker 1: It's interesting? Good on you might you go well appreciate it? 80 00:03:42,400 --> 00:03:45,760 Speaker 1: Pretty much? Is Gorman Auckland University, Medical Emeritus Professor. 81 00:03:46,240 --> 00:03:49,120 Speaker 2: For more from the Mic Asking Breakfast, listen live to 82 00:03:49,240 --> 00:03:49,800 Speaker 2: news talks. 83 00:03:49,840 --> 00:03:52,960 Speaker 1: It'd be from six am weekdays, or follow the podcast 84 00:03:53,040 --> 00:03:53,880 Speaker 1: on iHeartRadio.