NR 451 COMPLETE WEEKLY DISCUSSION PAPERS Week 2 Discussion

The Clinical Question (graded)

Formulating the PICO question and appraisal of journal article evidence (graded)

Your capstone change project began last week when you identified your problem. This week you will continue to develop your project:

• Formulate a significant clinical question that be the basis for your capstone change project.
• Identify each PICO element in the question.
• Relate how you developed the question?
• Describe the importance of this question to your clinical practice

ADDITIONAL INFORMATION 

Formulating the PICO question and appraisal of journal article evidence

Introduction

A PICO (Population-Intervention-Comparison-Outcome) question is a way of asking questions that helps you find relevant research evidence. It’s used in evidence-based medicine (EBM), which is a way of making decisions about how best to treat patients by using the best available evidence from medical research.

PICO – Population (or participant)

An intervention is an activity or treatment that is given to a participant. The comparison is the thing being compared to the intervention. Outcomes are measurements of how well the interventions work, and they can be either positive or negative.

As an example, imagine you’re interested in how effective walking is as a way to improve your health. You would want to study this using people who walk regularly as their normal activity so that they are able to compare it with other activities they may do (like running).

The PICO question for your research might be: What effect does walking have on blood pressure?

What is the sample size of the study?

The sample size of the study is dependent on the aims of the research. The larger the sample size, the more likely you are to detect differences between groups. However, this will also increase your chance of having a type II error (i.e., not finding a difference when one exists). Smaller samples are less likely to find differences but this can mean that if there was actually a difference it would be missed entirely because there were not enough participants in each group in order for it to be detected.

Was the sampling method appropriate, given the study aims and inclusion criteria?

In order to decide whether the sampling method was appropriate, you need to examine several factors.

• Was the sampling method appropriate, given the study aims and inclusion criteria? You should compare what was done with what you would have done. If there are differences, note where they are and consider whether they matter. For example, if you had wanted to look at a wider range of people than were included in this study (e.g., older adults), would it have affected your results? If so, how?

• How many participants did they include? Was this amount enough for their study design and aims? Did they only include enough participants to answer their specific research question(s)? Did they include too few or too many participants relative to their objective(s)? Did they leave out any important groups (e.g., minorities) from their sample population?

Were there any acceptable reasons for excluding participants from analysis?

• The study should be clear about why participants were excluded.

• The study should justify the exclusion of participants who were included in the analysis.

• The study should justify the exclusion of participants who were not included in the analysis.

Is it likely that the results are generalizable to other patients, settings, countries etc. in your context?

To answer this question, you need to consider the following:

• Study context: Who were the study participants? What was their age, gender, ethnicity and so on? Did they have any pre-existing conditions that may have influenced their results?

• Study setting: Where did the study take place? Was it in a hospital or clinic setting or was it done at home with patients who had been referred by their general practitioner or family doctor? This can make a difference to how successful treatments are. Patients who have been referred by their GP tend to have more severe cases of depression and be less likely to respond well to treatment than those who self-refer (i.e., go directly into therapy).

• Study design: Was it a randomized controlled trial (RCT) where patients were randomly allocated into groups receiving different therapies or no therapy at all? Or was it an observational study where researchers looked back over existing data sets available from large databases such as medical records kept by doctors and hospitals. RCTs are usually considered more reliable because they eliminate bias caused by patient selection but observational studies can shed light on questions like “what is happening in real life?”

Intervention (or exposure)

When you are reading the results of a study, you may notice that the authors have compared two groups to each other. For example, they might be comparing how long it takes for people to recover from an illness if they receive a particular treatment or not. In this case, one group received the treatment and another group did not.

If you want to know whether there was any difference between these two groups in terms of their recovery time (i.e., comparing intervention versus control), then what matters is whether both groups were allocated at random. This means that neither group had any advantage or disadvantage over the other: if either group were allocated randomly then neither would be expected to perform better or worse than another on average—this is called statistical equivalence (or using random allocation).

What was the intervention/exposure?

The next step is to determine what the intervention or exposure was. In other words, what was the treatment that was tested? For example, if you read an article about exercise interventions, then you know that they are not testing a diet change but rather some type of physical activity intervention.

The next question is whether or not the intervention had a positive effect (or not). It’s important to note that sometimes there can be no difference between groups during an experiment because both groups are identical (this is called a null hypothesis). However, if someone thinks their study results show something significant and writes it up for publication in an academic journal article then there must have been some kind of difference between groups at least sometime during the experiment—it could be large or small but it’s still significant from zero difference which would mean no effect at all!

How was each participant allocated to an intervention group or control group?

You should be able to answer the following questions:

• What was the method of allocation?

• Randomization may be appropriate if it is done by a computer or other comparable method, and results in each participant being equally likely to be assigned either intervention or control group. For example, patients with ischemic stroke could be randomized into two groups (intervention and no intervention) through the use of a computerized random number generator. On the other hand, if clinicians were allowed to decide whether or not they wanted their patients to receive an intervention after seeing them, this would make randomization inappropriate as it would lead to bias. In addition, any trial that allows participants’ preferences for one treatment over another may also render randomization invalid because there will always be some people who wish they had been allocated differently by chance alone due to their personal preference (i.e., people have “lucky numbers”). This means that these trials cannot truly measure benefits from receiving an intervention versus not receiving one because only those who are happy with their allocation will get better outcomes than those who do not receive any treatment at all; hence why we call such studies “convenience” samples instead of truly randomized controlled trials (RCTs).

Comparison (or control)

Comparison (or control) groups are used in studies to compare the effectiveness of an intervention with a control group. A comparison group is therefore not receiving the treatment or intervention being studied. You should always check if there was an appropriate comparison/control group and whether it was used appropriately for this type of study. For example, if you’re looking at whether giving children cuddly toys helps them to sleep better at night than giving them no toys, then it would not be appropriate to select healthy children who did not receive any cuddly toys as your comparison/control group! It would be more appropriate to find another group of sick children who received another type of treatment from another doctor and see how their sleep compares with those receiving nothing at all (the control).

Outcome (or outcome measure)

The outcome measure is the thing you’re interested in measuring. It can be a primary outcome measure or a secondary outcome measure, and it may be continuous (like pain) or dichotomous (like death). If there were multiple time points for measurement, was the outcome measured during the study, at baseline, or at follow up?

Conclusion

PICO is a useful framework for conducting a systematic review. It helps researchers to identify the population, intervention, comparison and outcome in the studies they are considering for inclusion in their review. The PICO question can be used as an appraisal tool to evaluate whether the evidence presented in journal articles is valid or not.

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