Chapter 10: Evidence-Informed Decision Making

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Hello everyone, and welcome back to the Deep Dive.

Today, I want to start by admitting something that I think a lot of us feel but are maybe too afraid to say out loud, especially if you're in nursing school or just starting out in your career.

There's this distinct feeling of dread when you open a textbook and see a chapter title with words like research or methodology or heaven forbid statistics.

You just know you're about to get hit with a wall of acronyms and math.

It's the eat your vegetables chapter of every single nursing textbook.

Everybody knows it's good for them.

You know it's necessary, but nobody is picking it off the menu first.

Exactly.

It feels so dense.

It feels dry.

And frankly, it could feel really disconnected from the actual human work of nursing.

You want to be holding a patient's hand or solving a community health crisis, not trying to calculate a confidence interval.

But today, we are going to

A Canadian Perspective, the fifth edition.

The chapter is called Evidence -Informed Decision Making in Community Health Nursing,

and it's by Jackie Mirrison and Rebecca Gannon.

And if we do our job right today, we're going to show you that this chapter isn't really about math at all.

It's about defense.

Defense.

I like that.

Yeah.

It's about defending your patients from bad or outdated care.

It's about defending your community from wasted resources.

And honestly, it's about defending yourself from just guessing.

I love that framing.

Defense.

Because the stakes are actually incredibly high, aren't they?

We're not just memorizing definitions for a quiz here.

Not at all.

We're learning the operating system for modern health care.

And to kick things off, I want to bring up a specific scenario right from the start.

It's a case study from the chapter that I want us to just kind of keep in the back of our minds as we navigate through all this theory.

You're thinking of the dementia palliative care scenario.

That's the one.

Imagine you're a community health nurse, a CHN.

You're sitting on a committee dedicated to improving dementia care.

You've got people around the table.

They're all realizing that patients with dementia who are nearing the end of life just aren't getting the right kind of support.

It's a mess.

People are suffering.

And the committee looks at you, the nurse, and says, we need a guideline.

What should we do?

And that is a terrifying moment if you don't have these skills.

It really is.

If you just rely on your gut feeling or what you saw one time in a clinical placement, you might suggest something that sounds nice, but in reality could actually make things worse.

Or you might just reinvent the wheel.

Totally.

You could spend six months writing a policy from scratch that already exists and is probably done better by someone else.

Right.

So keep that CHN in your mind.

How does she answer that question?

How does she find the answer without drowning in information?

That is what this chapter is all about.

It's the roadmap.

And it starts with a really important shift in language.

We've all heard the term evidence -based practice.

It's a huge buzzword.

But this chapter uses evidence -informed decision -making or EIDM.

Is that just semantics or is there a real meaningful difference there?

Oh, it's a profound difference.

And it's really the first big aha moment in the text.

Evidence -based has a bit of a rigid history.

In the early days, there was this fear that it meant cookbook medicine.

Cookbook medicine.

Yeah.

You find a study, the study says do X, so you just do X.

You become a robot.

You effectively turn off your clinical brain and just follow the algorithm.

Which is terrifying because humans are not algorithms and communities are definitely not algorithms.

Precisely.

So evidence -informed softens that edge.

It implies that the evidence is a guest at the table.

A very important guest, maybe even the guest of honor, but it is not the dictator.

It informs the decision.

It doesn't dictate it.

Okay.

So it's a key ingredient, but not the whole recipe.

That's a great way to put it.

The authors present this really helpful visual model.

It's figure 10 .1 in the text.

I want you to picture a set of intersecting circles, like a Venn diagram.

Okay.

I'm visualizing.

We've got the decision right in the middle where they all overlap.

What are the circles feeding into it?

So obviously one big circle is research evidence.

That's the studies, the data, the science.

But, and this is the key, there are three other circles that are the same size.

They are equal partners.

Equal partners.

One is public health resources.

Oh, the reality check.

Yeah.

Do we have the money?

Do we have the staff?

Do we have the equipment?

You can have the best, most evidence -based cure in the world, but if it costs a million dollars a dose and you're working in a publicly funded community clinic.

It's not happening.

It's just not relevant.

Correct.

The next circle is client and community context.

What's actually happening on the ground here?

What are the political realities of this town?

What are the specific health issues in this neighborhood versus the one two blocks over?

And the third one.

The third one, which I think is so often overlooked, is client preferences.

What do the people we're serving actually want?

And then the text shows this fourth element that sort of wraps around all of this, doesn't it?

The nurse's own clinical expertise.

Yes,

the nurse is the synthesizer.

You are the mixer.

You're the one at the table taking these raw ingredients, the science, the budget, the local context, the patient's desire, and you use your professional expertise to bake the cake, so to speak.

The book uses a really specific example to illustrate this, this potential clash between evidence and reality.

It involves postpartum weight loss.

Can we walk through that?

Because I think it perfectly highlights why just being evidence -based isn't enough.

It's a perfect example.

So the issue is weight retention after pregnancy.

The text notes that about 35 % of Canadian women enter pregnancy already overweight or obese, and a lot of them gain more than the guidelines recommend.

Which has all sorts of long -term health implications.

Exactly.

So CHN goes looking for a solution, an evidence -informed solution.

She finds a systematic review, which we'll talk more about later, by a group led by Shara Folley from 2017.

And what does the science that research evidence circles say?

The science is very clear.

E -health interventions, so using web -based tools, apps, text message support, they are statistically effective at new mothers lose weight.

The numbers prove it works.

Okay.

So in that old evidence -based robot world, the nurse would just print out the sign -up sheet for the text message program and hand it to every new mom.

Job done.

Check the box.

Right.

But we're not robots.

We're doing events -informed decision -making.

So now we have to look at those other circles.

Let's start with client preference.

Okay.

Imagine a new mother.

She is completely sleep deprived.

Maybe she's struggling with breastfeeding.

Maybe she has some postpartum mood issues happening.

Does she really want her phone buzzing every four hours with a text message reminding her about her BMI?

Oh, that could be incredibly intrusive.

I could see that actually increasing her stress levels, which might be even worse for her long -term health than the extra few pounds.

Exactly.

And then you have to look at the local context circle.

Does this mom even have a smartphone?

Does she have a reliable data plan?

If you're working with a low -income population or maybe a newcomer population with low digital literacy,

this scientifically proven intervention is absolutely useless.

It might even make things worse from an equity standpoint.

It could widen the gap because only the wealthier, tech savvy moms with the latest iPhones get the help.

Precisely.

So EIDM is the process of saying, okay, the research says text messages can work, but for this mom in this situation with her preferences, maybe we're going to try a walking group instead.

So you're filtering the evidence through the reality of that specific person's life.

That is the absolute essence of it.

Now I want to play devil's advocate here for just a minute.

This sounds, well, it sounds exhausting.

It sounds like a lot of work to check all these circles for every single decision.

If I've been a nurse for 20 years, why can't I just rely on my clinical expertise?

That's one of the circles after all.

Why do I need to go digging up the research at all?

That is the big temptation, isn't it?

I've seen it all.

I know it works.

But the text hits us with a really critical piece of data right here.

They cite a meta analysis, which is a study of studies by Heater, Becker, and Olson.

Now this is from 1988, so it's a classic foundational finding in this field.

Okay.

So this has been known for a long time.

What did they find?

They compared patients who received care that was based on research findings versus patients who received routine care, which is basically, you know, whatever the nurse felt like doing or whatever the hospital tradition was.

This is how we've always done it approached.

Exactly that.

And the patients who received the research -based care made sizable gains in behavioral knowledge and physiological outcomes.

Sizable gains.

So we're not talking about a tiny 1 % difference here.

We're talking about people actually getting better faster, staying healthier, and understanding their conditions better.

It's a huge difference.

It validates that our gut feeling, as good as it might be, is often wrong.

Or at the very least, it's not as optimized as we think it is.

The evidence really does lead to better care.

But there's a problem.

A really big one.

If research leads to these sizable gains, you'd think every nurse would be glued to the latest medical journals every morning.

But the chapter has a whole section on barriers.

And there's one statistic in here that I literally highlighted three times.

It just stopped me in my tracks.

I know exactly which one you mean.

It's the time lag.

Listen to this, everyone.

The text states that there's a substantial time lag between when technical information is generated, so when a study proves something works, and when it is actually used in practice.

How long do you think that gap is?

It's eight to 17 years.

17 years.

I just want to sit with that for a second.

That is an entire generation.

That means a child born today could be graduating high school before a discovery made today actually reaches their bedside.

It's staggering when you think about it.

It means that right now in hospitals and clinics all across Canada, patients are receiving care that in some cases might be two decades out of date.

We are literally practicing in the past.

How is that even possible?

I mean, we're in the information age.

Why?

The text breaks it down really well.

It's basically a pipeline problem with multiple weeks.

First, there's just the volume, sheer volume.

There is simply too much research being published.

Information overload.

Complete overload.

Thousands of articles a day.

No single human being can read it all.

You could spend 24 hours a day just reading and you'd still fall further and further behind.

Okay, so that's the volume overload.

There's the access issue.

We talked about public resources being one of the circles.

Right, so you finally find the perfect article.

You click on the link and a pop -up asks you for $45 to read the PDF.

The paywall.

The paywall.

If your organization, your hospital or clinic doesn't have a subscription to that journal, you're just locked out.

That's a massive organizational barrier.

And then there's the skill gap, which brings us right back to that fear we talked about at the very beginning.

Let's say you get past the paywall.

You open the article and you see a bunch of Greek letters and P values and something called a forest plot, if you don't know how to interpret that.

You're just going to close the tab.

You'll go back to doing what you've always done because it's familiar and it feels safer than trying to apply something you don't understand.

Which is why this chapter is so important.

It's trying to give us the tools to actually shrink that 17 -year gap.

It breaks the whole EIDM process down into seven manageable steps.

It really demystifies it.

Okay, let's get into the how.

We've established the what and the why.

Now we need the user manual.

The text lays out a seven -step process.

Step one is define.

Which sounds so simple, but it's honestly where most people fail right out of the gate.

You can't just go to a database and type dementia into the search box.

You get 10 million results.

Exactly.

You'll drown.

You need a structured, answerable question.

And this is where the famous acronym PCO comes in.

I feel like PI is tattooed on the brain of every nursing student.

But let's break it down again, maybe in the context of our case study.

The CHN looking for a dementia palliative care guideline.

Perfect.

So PLICIO helps you build a quantitative question, a question about numbers and effectiveness.

P is for population.

In our case, that would be people with dementia nearing the end of life.

Simple enough.

I is for intervention.

That's the thing you're thinking of doing.

So a palliative care approach or maybe specific palliative guidelines.

Okay.

C is for comparison.

This is usually routine care or standard care, the what we've always done option.

And O is for outcome.

What are we hoping to achieve?

It could be a number of things.

Improved quality of life, better pain management, or higher family satisfaction.

So you put it all together and your searchable question becomes, in people with dementia, P, does a palliative care approach, I, compared to routine care, C, improve quality of life, O.

That is so much more focus.

It's laser focus compared to just typing dementia help.

But the text also mentions another format, PS questions.

What's the difference there?

So PS is for qualitative research.

Sometimes we don't care about the numbers.

We care about the experience, the why and the how.

P is still population, but S stands for situation.

So a question might be, for indigenous women, P, what is the experience of the barriers to the cervical cancer screening S?

It helps you find the stories behind the statistics.

Right.

So once we have our question, our PCO or our PS, we're ready for step two, which is search.

And this is where the chapter introduces my absolute favorite visual in the whole book.

The success pyramid.

The success pyramid is your survival strategy.

I tell all my students, print this out, tape it to your monitor.

It's a hierarchy of pre -processed evidence.

And the golden rule here is so simple.

Start at the top.

Always, always start at the top.

Why?

What's the logic there?

Because the top of the pyramid is where the work has already been done for you.

The evidence is synthesized.

It's appraised.

It's ready to use.

The bottom is the raw data.

The single study is where you have to do all that work yourself.

If you start at the bottom, you will drown.

It's that simple.

Okay.

Let's climb this pyramid then.

What's at the very, very peak?

The highest point.

The top level is systems.

And this is sort of the futuristic dream that we aren't quite at yet in most places.

The sci -fi level.

Pretty much.

Imagine an electronic health record, an EHR, that is so smart that when you enter a patient's data, their age, their diagnosis, their lab results, it automatically links you to the relevant up -to -date guideline.

It might flash a warning.

Based on this patient's blood pressure, evidence suggests you should be doing X.

The text says this is still pretty rare, especially in community health.

It is.

It's coming, but it's not widely implemented, so we usually have to step down to the second level.

Summaries.

These are your clinical practice guidelines.

Like the ones from the RNAO, the Registered Nurses Association of Ontario.

I see those cited all the time.

Exactly.

Or the guidelines from the Canadian Task Force on Preventive Health Care.

A summary or a guideline is a document where a whole panel of experts has already done the hard work.

They've read all 10 ,000 studies on a topic, they've argued about them, they've graded the quality, and they've written a clear set of recommendations.

Do this, don't do that.

So if you find a current high -quality guideline from a reputable source,

do you even need to look further down the pyramid?

Generally, no.

Your search could be done right there.

If the guideline answers your question and it's recent, you've just saved yourself potentially hundreds of hours of work.

Wow.

Okay, but what if there is no guideline?

What if my topic is too new or too niche?

Then you take one more step down the pyramid to synopsis of synthesis.

That is a mouthful.

It really is.

Let's break it down.

A synthesis is a systematic review.

We'll get to that in a second.

A synopsis is basically the Cliff's Notes or Cole's Notes version of that review.

And the text highlights a specific Canadian resource here that is an absolute goldmine for community health nurses.

Which one is that?

It's a website called Health Evidence.

I actually looked this up while I was reading.

It's fantastic.

It's incredible, isn't it?

Health Evidence is a registry of systematic reviews that are specifically relevant to public and community health.

But they don't just list them.

They have independent reviewers who rate the quality of every single review on a scale of 1 to 10.

Oh, that's huge.

It's game -changing.

And then they write a short two -page summary for you.

So instead of having to read a 50 -page dense academic paper, you can read a two -page summary that tells you, hey, this is a high -quality review.

We rated it an 8 out of 10.

And here's the bottom line of what they found.

It completely solves that volume overload and skill gap problem we were talking about.

It's designed to do exactly that.

Okay.

So if we can't find a synopsis, we go down to the next level, synthesis.

Right.

This is the full systematic review itself.

This is what you find in databases like the Cochrane Library or the Campbell Collaboration.

A systematic review is a massive research project where the researchers try to find every single study ever written on a specific PIO question.

Every single one.

They try published studies, unpublished studies, studies in English, studies in other languages.

They cast the widest possible net and then combine all the results into one massive, powerful answer.

The text makes a point of distinguishing between the Cochrane Library and the Campbell Collaboration.

Yes.

And it's a really useful distinction for community nurses.

The Cochrane Library is the gold standard for medical effectiveness.

Does this drug work?

Does this type of wound dressing work better than another?

More clinical questions.

Exactly.

The Campbell Collaboration, on the other hand, focuses on social interventions, things like education, social welfare, and criminal justice.

So if your question is about the social determinants of health -like, what's the evidence for a certain housing policy or an early childhood education program,

Campbell is often a much better bet than Cochrane.

That's a great practical tip.

And then below that, way down at the wide bottom of the pyramid, we have synopsis of studies, and finally at the base, just studies.

Right.

And studies are the single original research articles you'd find on a big database like PubMed or CNHL.

This is the Wild West of evidence.

The Wild West.

Yeah.

Because it's just one study.

It might have been poorly designed.

It might be biased.

It might have had a really small sample size.

It might just be flat out wrong.

If you find a single study, you have to be so careful.

You have to appraise it very critically yourself.

Which brings us perfectly to step three, appraise.

This is the part that I think really scares people.

You've found a study.

You have the PDF open on your screen.

How in the world do you know if it's good quality or if it's just garbage?

This is where we need to put on our detective hats.

The text breaks this down into two big questions.

Is it valid and is it important?

So is the message solid and do the results actually matter?

Exactly.

Let's stick with quantitative intervention studies first, the ones with all the numbers.

When we're looking at validity, there are a few red flags we're searching for.

Okay.

What's red flag number one?

The first one is randomization.

For a study comparing two treatments, the participants have to be randomly assigned to the groups.

Was it truly random, like a coin flip?

Or did the doctor, maybe subconsciously, put all the healthier looking patients in the new treatment group and all the sicker patients in the control group to make the new drug look better?

That would be cheating.

It would be, and it happens.

That's why we also look for something called concealment.

This means the person enrolling the patients in the study shouldn't know which group the next patient is going to be assigned to.

It prevents that bias, conscious or not.

Okay, that makes sense.

What's next?

Next, we look at intention to treat.

This one always turns students up.

Intention to treat.

It sounds so clunky.

It does, but the concept is so important.

Think of it like a diet study.

You recruit 100 people for a really strict new keto diet.

After two months, 50 people have stuck to it perfectly.

But the other 50 gave up after three days and went right back to eating pizza and ice cream.

Okay.

Now, if I'm a researcher and I only analyze the results of the 50 people who stuck to the diet perfectly, what are my results going to look like?

They'll look amazing.

Yeah.

Look, everyone in our diet study lost 20 pounds.

Exactly.

But it's a lie, isn't it?

Because in the real world, 50 % of people are going to quit this diet.

Intention to treat analysis means you analyze everyone in the group they were originally assigned to, regardless of whether they actually followed through.

You include the pizza eaters in the keto group's data.

So it makes the results look worse, but it's actually a much more honest and realistic picture.

Of how the intervention would work in the real messy world.

Precisely.

It prevents that kind of biased cherry picking of only the best results.

Okay.

Let's talk STAPS, the wetter forecast analogy from the text about confidence intervals or CI.

This was so helpful.

Yes.

This is all about precision.

Imagine I'm a weather forecaster.

You ask me, what's the temperature going to be in tomorrow?

And I say, well, I am 95 % confident will be somewhere between minus 40 degrees Celsius and plus 40 degrees Celsius.

Technically, you're almost certainly right.

But that information is completely useless to me.

I don't know if I need a parka or a swimsuit.

Right.

That is a very wide confidence interval.

It means the study's result is imprecise and shaky.

But what if I say I am 95 % confident it will be between 20 degrees and 22 degrees?

Now that's useful.

I can plan my picnic.

It's a precise estimate.

That is a narrow confidence interval.

When you're looking at a study's results, don't just look at the main number.

Look at the interval around it.

If the range is huge, the result isn't very reliable.

And what about that so what factor?

The text makes a big deal about the difference between statistical significance and clinical importance.

This is huge.

A result can be statistically significant, which usually means it has a p -value less than 0 .05.

All that tells you is that the result probably didn't happen by random chance.

But is it clinically important?

Does it matter to a real human?

Give me an example.

OK.

A new painkiller is tested, and it reduces patients' pain scores by 0 .5 on a 10 -point scale.

That result might be statistically significant.

So it's technically a reduction.

It is.

The math says it's real.

But if you ask a patient, do you feel any better if your pain goes from an 8 out of 10 to a 7 .5 out of 10?

They will probably say no.

It's not a big enough difference to actually matter to the person experiencing the pain.

So as nurses, we have to look past the p -value and ask, is this change meaningful in a patient's life?

Correct.

And this leads into another area where we have to be detectives.

RRR versus ARR.

Relative risk reduction versus absolute risk reduction.

This is a classic marketing trick used by drug companies.

Oh, I love busting marketing tricks.

Let's break it down.

OK.

Let's say there's a rare disease.

In an untreated group, only two people out of 100 get it each year.

So your baseline risk is 2%.

Right.

A new drug comes out.

In the group that takes the drug, now only one person out of 100 gets the disease.

So the new risk is 1%.

The absolute difference between 2 % and 1 % is?

1%.

You've reduced the absolute risk by 1%.

You save 1 % for every 100 people you treat.

That is the ARR, the absolute risk reduction, 1%.

But the drug company's marketing team says, wait a minute, we went from two cases down to one case.

We cut the risk in half.

That's a 50 % drop.

And their headline says, new wonder drug cuts disease risk by 50%.

That's the RRR, the relative risk reduction.

It sounds so much more impressive, but it hides the fact that the disease was really rare to begin with.

You always, always have to look for the absolute numbers to understand the true impact.

And that leads directly to the NNT, the number needed to treat.

Which is just the inverse of the absolute risk.

If the ARR is 1 % or 1 in 100, then the NNT is 100.

You have to treat 100 people with this drug, potentially exposing all of them to side effects, just to prevent one person from getting the disease.

Is it worth it?

Maybe.

Maybe not.

That's a judgment call.

And that's nursing judgment.

That's EIDM.

We've been very focused on numbers here.

But the text pivots to appraising qualitative research.

And this is a whole different ball game.

You can't calculate a confidence interval on someone's story about grief.

No, you can't.

For qualitative work, we're not looking for numbers.

We're looking for things like credibility and transferability.

We look at the methodology they used.

Was it phenomenology, trying to understand a lived experience?

Was it grounded theory, trying to develop a new theory or model?

Or ethnography, describing a culture?

And the text says we need to look for something called saturation.

What's that?

Saturation is the key quality marker in a lot of qualitative research.

It means the researcher kept interviewing people or observing until they stopped hearing new things.

So they're not getting any new information.

Exactly.

If you only interview five people about an experience, you might miss a huge piece of the puzzle.

But if you interview 50 people, and the last 10 people you talked to all basically said the same things as the people before them, you can be pretty confident you've reached saturation.

You have the full picture.

Speaking of the full picture, there's a spotlight section in this chapter that I think is incredibly important to talk about.

It's a box labeled Yes, But Why.

And it focuses on Indigenous health and a specific methodology called PR participatory action research.

This section is absolutely critical.

It's the perfect bridge between the abstract idea of research methods and the real world pursuit of social justice.

It discusses a study by Maher and her colleagues regarding cervical cancer screening.

And the context here is it's disturbing, but sadly not surprising for anyone who knows about health inequities in Canada.

Cervical cancer rates are going down in the general population, but they remain significantly higher for Indigenous women.

Right.

So a traditional non -PR researcher might look at that statistic and say, Well, the numbers show Indigenous women aren't getting cap tests.

The problem must be a lack of knowledge.

We need to educate them.

Let's make a brochure.

Which is the deficit model.

It assumes the patient is the one with the problem that they just don't know any better.

Exactly.

But Maher and her team used PAR participatory action research.

This means they didn't just go and study on the community.

They partnered with the community.

The community members were co -researchers shaping the questions and interpreting the findings.

And what did that collaborative approach reveal?

It revealed that a lack of education wasn't the problem at all.

The women knew about cancer.

The real barriers were structural.

There was no recall system.

Nobody was calling them to remind them it was time for screening.

There was no transportation to get to the clinics in these rural areas.

And most importantly, there was a deep and historically justified distrust of the health care system.

A distrust stemming from the legacy of colonialism.

Yes.

From negative personal experiences with the system, from intergenerational trauma, a fear that the interaction would be racist or dismissive.

A brochure doesn't fix racism.

A brochure doesn't build a bus route.

So the PAR approach didn't lead to a brochure.

It led to completely different solutions.

Completely.

It led to recommendations for structural changes.

Things like embedding screening programs into community events where women already felt safe and comfortable.

Incorporating indigenous perspectives on health and the body into the care itself.

Taking the time to build rapport and trust.

The text connects this directly to the community health nursing standard to practice.

Specifically standard six, which is about promoting equity.

And standard four about professional relationships, which includes truth and reconciliation.

It shows that your choice of research method like choosing PAR instead of a simple survey is actually a profound ethical choice.

It absolutely is.

If they had just sent out a survey, they would have completely missed the truth.

Okay.

We are moving steadily through the seven steps.

We've defined our question, searched the pyramid, and appraised the quality.

Step four is synthesize.

This is where you have to put all the pieces together.

This is the puzzle solving part.

You might have found one high quality guideline that says, yes, do this.

But also a newer single study that says, no, that might be harmful.

You have to weigh them.

And the rule of thumb is to trust the evidence from higher up the pyramid.

So a big systematic review trumps a small single study.

Usually, yes.

And a more recent guideline trumps one from 10 years ago.

You have to look at the whole body of evidence, not just one piece.

Then we get to step five, adapt.

This brings us right back to those intersecting circles from the beginning.

The text mentions a tool, the applicability and transferability tool.

Right.

This is where you ask those practical on the ground questions.

Is my population here in rural Saskatchewan similar enough to the study population of urban Torontonians?

Is this intervention politically and socially acceptable in my community?

The text gives that great example of a school -based sexual health program.

The research, the evidence might be crystal clear that a certain program is effective at reducing STIs.

Right.

The science is solid.

But if the local school board is very conservative and the parent community is actively protesting it, you can't just force it in because the evidence says so.

You have to adapt.

Absolutely.

The adapt phase might mean you need to do a year of community engagement and education with parents before you can even think about rolling out the program.

You're adapting the implementation to the local context.

Which leads to step six, implement.

The text flat out calls this the hardest part.

Because change is hard.

People and organizations are resistant to change.

You can't just send out a memo that says, new policy starting Monday and expect everyone to magically follow it.

They mentioned the Grimshaw review here, which I found so fascinating because it kind of goes against our intuition.

We usually think more is better.

Like if I want nurses on my unit to wash their hands more, I should do a poster and a lecture and an email campaign and an educational video.

That's the multifaceted intervention approach.

But what Grimshaw's review found was that these big complex multifaceted interventions aren't consistently better than simple single component ones.

So more isn't always more.

Not necessarily.

Sometimes a simple reminder, a checklist on the wall, an alert in the computer system is just as effective as a massive time -consuming education campaign.

It all depends on what the actual barrier is.

That makes sense.

It depends on the why.

Exactly.

If the barrier is, I just forgot, our reminder works perfectly.

If the barrier is, I don't know how, then you need education.

If the barrier is, the soap dispenser is always empty, you don't need education, you need a better supply chain.

No amount of posters fixes an empty soap dispenser.

That's why you need an environmental scan before you implement anything.

Find the real problem first.

And then finally, step seven, evaluate.

Did our change actually work?

And crucially, did it have any unintended negative consequences?

Did our new program to reduce falls in the elderly actually work?

Or did it just lead to an increase in the use of physical restraints?

You have to keep measuring.

It's a cycle.

I want to bring this all home by returning to our CHN on the dementia committee.

The chapter ends with a case study that walks through this whole process, a review by drippos and colleagues.

This really wraps it all up with a bow.

It does.

It shows the seven steps in action.

So the committee needed a palliative dementia guideline.

Step one, define.

They created a PICO question.

Population, dementia, intervention, palliative care, outcome, a guideline.

Step two, search.

They didn't just use a generic search engine.

They used a specific tool in the PubMed database called clinical queries, which is designed to find this kind of evidence.

They searched dementia and palliative and guideline.

Okay.

Step three, appraise.

They found a systematic review by drippos, but they didn't just blindly trust it.

They looked at how drippos and the team appraise the guidelines they found.

And they saw that drippos used a standardized tool called the GRITU.

GRITU.

It stands for appraisal of guidelines for research and evaluation.

It's the gold standard tool for grading the quality and rigor of clinical practice guidelines.

They could trust the appraisal and what did drappos find?

They found that the NICE guideline from the UK, that's the National Institute for Health and Clinical Excellence, was a very high quality.

It was comprehensive, it was evidence -based, and it covered the key domains of physical, psychological, social, and spiritual care.

So then, step four, synthesize.

They decided this NICE guideline was the best available piece of evidence in the world on this topic.

Step five, adapt.

They reviewed the UK guideline to see if it would fit the Canadian healthcare context.

It did.

Step six, implement.

They had a solid plan to roll it out to the staff.

And step seven, evaluate.

They put measures in place to track if patient care and family satisfaction actually improved after the new guideline was implemented.

They didn't have to write a new guideline from scratch, which could have taken years.

They saved months and months of work, and more importantly, they ensured their patients were getting world -class, evidence -informed care, all just by knowing how to properly define, search, and appraise.

That is the real power of EIDM, isn't it?

It's not about doing more work.

It's about doing smarter, more effective work.

And the text makes it clear that this isn't just a NICE idea.

It's a professional responsibility.

It's explicitly linked to standard seven of the community health nursing standards in Canada.

We have a professional duty to use the best evidence available to us.

It's not optional.

As we start to wrap up, I just want to leave our listeners with that one thought we started with, that one haunting statistic,

the 17 -year gap.

It really is the most important number in the whole chapter.

If we know it can take up to 17 years for new research to become routine practice,

that means there is a solution existing right now, today, locked away in a PDF,

buried in some database that could save a life or dramatically improve someone's health.

But it won't be standard practice until the year 2043.

Unless you find it.

Unless you find it.

That is the challenge to every nursing student, every new nurse, every experienced nurse listening to this.

Don't wait for the textbook to be updated in five years.

Don't wait for the official policy to change.

Learn these skills so you can find the answer for your patient, for your community, now.

Be the person who builds the bridge.

Be the one who closes that gap.

Thank you so much for joining us on this deep dive into chapter 10.

We really hope that research feels a little less like a scary monster now and maybe a little more like a superpower.

Go find that evidence.

This has been the Last Minute Lecture Team.

Thanks for listening and take care.