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動画の字幕をクリックしてすぐ単語の意味を調べられます!
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We live in a world where the collection of data
is happening 24 hours a day, seven days a week,
365 days a year.
This data is usually collected by what we call a front-desk specialist now.
These are the retail clerks at your favorite department stores,
the cashiers at the grocery stores,
the registration specialists at the hospital
and even the person that sold you your last movie ticket.
They ask discreet questions, like: "May I please have your zip code?"
Or, "Would you like to use your savings card today?"
All of which gives us data.
However, the conversation becomes a little bit more complex
when the more difficult questions need to be asked.
Let me tell you a story, see.
Once upon a time, there was a woman named Miss Margaret.
Miss Margaret had been a front-desk specialist
for almost 20 years.
And in all that time, she has never, and I do mean never,
had to ask a patient their gender, race or ethnicity.
Because, see, now Miss Margaret has the ability to just look at you.
Uh-huh.
And she can tell if you are a boy or a girl,
black or white, American or non-American.
And in her mind, those were the only categories.
So imagine that grave day,
when her sassy supervisor invited her to this "change everything" meeting
and told her that would have to ask each and every last one of her patients
to self-identify.
She gave her six genders, eight races and over 100 ethnicities.
Well, now, Miss Margaret was appalled.
I mean, highly offended.
So much so that she marched down to that human-resource department
to see if she was eligible for an early retirement.
And she ended her rant by saying
that her sassy supervisor invited her to this "change everything" meeting
and didn't, didn't, even, even
bring, bring food, food, food, food.
(Laughter)
(Applause) (Cheers)
You know you've got to bring food to these meetings.
(Laughter)
Anyway.
(Laughter)
Now, that was an example of a healthcare setting,
but of course, all businesses collect some form of data.
True story: I was going to wire some money.
And the customer service representative asked me
if I was born in the United States.
Now, I hesitated to answer her question,
and before she even realized why I hesitated,
she began to throw the company she worked for under the bus.
She said, "Girl, I know it's stupid, but they makin' us ask this question."
(Laughter)
Because of the way she presented it to me,
I was like, "Girl, why?
Why they makin' you ask this question?
Is they deportin' people?"
(Laughter)
But then I had to turn on the other side of me,
the more professional speaker-poet side of me.
The one that understood that there were little Miss Margarets all over the place.
People who were good people, maybe even good employees,
but lacked the ability to ask their questions properly
and unfortunately, that made her look bad,
but the worst, that made the business look even worse
than how she was looking.
Because she had no idea who I was.
I mean, I literally could have been a woman who was scheduled to do a TED Talk
and would use her as an example.
Imagine that.
(Applause)
And unfortunately,
what happens is people would decline to answer the questions,
because they feel like you would use the information
to discriminate against them,
all because of how you presented the information.
And at that point, we get bad data.
And everybody knows what bad data does.
Bad data costs you time, it costs you money
and it costs you resources.
Unfortunately, when you have bad data,
it also costs you a lot more,
because we have health disparities,
and we have social determinants of health,
and we have the infant mortality,
all of which depends on the data that we collect,
and if we have bad data, than we have those issues still.
And we have underprivileged populations
that remain unfortunate and underprivileged,
because the data that we're using is either outdated,
or is not good at all or we don't have anything at all.
Now, wouldn't it be amazing if people like Miss Margaret
and the customer-service representative at the wiring place
were graced to collect data with compassionate care?
Can I explain to you what I mean by "graced?"
I wrote an acrostic poem.
G: Getting the front desk specialist involved and letting them know
R: the Relevance of their role as they become
A: Accountable for the accuracy of data while implementing
C: Compassionate care within all encounters by becoming
E: Equipped with the education needed to inform people
of why data collection is so important.
(Applause)
Now, I'm an artist.
And so what happens with me
is that when I create something artistically,
the trainer in me is awakened as well.
So what I did was, I began to develop that acrostic poem into a full training
entitled "I'm G.R.A.C.E.D."
Because I remember, being the front-desk specialist,
and when I went to the office of equity to start working,
I was like, "Is that why they asked us to ask that question?"
It all became a bright light to me,
and I realized that I asked people and I told people about --
I called them by the wrong gender, I called them by the wrong race,
I called them by the wrong ethnicity,
and the environment became hostile,
people was offended and I was frustrated because I was not graced.
I remember my computerized training,
and unfortunately, that training did not prepare me to deescalate a situation.
It did not prepare me to have teachable moments when I had questions
about asking the questions.
I would look at the computer and say, "So, what do I do when this happens?"
And the computer would say ...
nothing, because a computer cannot talk back to you.
(Laughter)
So that's the importance of having someone there
who was trained to teach you and tell you what you do
in situations like that.
So, when I created the "I'm G.R.A.C.E.D" training,
I created it with that experience that I had in mind,
but also that conviction that I had in mind.
Because I wanted the instructional design of it
to be a safe space for open dialogue for people.
I wanted to talk about biases,
the unconscious ones and the conscious ones,
and what we do.
Because now I know that when you engage people in the why,
it challenges their perspective, and it changes their attitudes.
Now I know that data that we have at the front desk
translates into research that eliminates disparities and finds cures.
Now I know that teaching people transitional change
instead of shocking them into change
is always a better way of implementing change.
See, now I know people are more likely to share information
when they are treated with respect by knowledgeable staff members.
Now I know that you don't have to be a statistician
to understand the power and the purpose of data,
but you do have to treat people with respect and have compassionate care.
Now I know that when you've been graced,
it is your responsibility to empower somebody else.
But most importantly, now I know
that when teaching human beings
to communicate with other human beings,
it should be delivered by a human being.
(Applause)
So when y'all go to work
and y'all schedule that "change everything" meeting --
(Laughter)
remember Miss Margaret.
And don't forget the food, the food, the food, the food.
Thank you.
(Applause) (Cheers)
Thank you.
(Applause)
コツ:単語をクリックしてすぐ意味を調べられます!

読み込み中…

【TED】タミキア・ミズレイディ・スミス: 困難な会話に対処できる従業員の育て方 (How to train employees to have difficult conversations | Tamekia MizLadi Smith)

428 タグ追加 保存
林宜悉 2018 年 8 月 21 日 に公開
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