Highfield, Martha E. F. PhD, RN
Section Editor(s): Howard, Patricia Kunz PhD, RN, CEN, CPEN, NE-BC, FAEN; Shapiro, Susan E. PhD, RN
ARE HOLIDAY Santa Claus (SC) activities evidence based? This is a priority issue for those of us who've been nice, not naughty. In this article, I review the strength of current evidence supporting the existence of SC, discuss various applications of that evidence, and suggest new avenues of investigation.
What is the evidence for SC and what is its relative strength? We have no meta-analyses, no systematic reviews of the literature, no evidence-based clinical guidelines, and no randomized controlled trials (RCTs). In other words, missing are all the strongest levels of evidence (Emergency Nurses Association, 2011).
What do these levels of evidence mean? Meta-analyses are those in which the researchers combine the data from many individual RCTs as if they were one big study. Honestly on the SC issue we're not there yet because we haven't generated any RCTs that I can find in an online library search of PubMed, CINAHL, ERIC, and other databases.
Systematic reviews and evidence-based clinical practice guidelines that summarize reviews of existing research are also strong evidence. Sadly no examples of these related to SC could be found at any of the usual sources: Cochrane library (http://www.cochrane.org/), Joanna Briggs Institute (http://www.joannabriggs.edu.au/), or National Guidelines Clearinghouse (http://www.guidelines.gov/).
Randomized controlled trials are next in strength. In these experiments the researchers randomly assign subjects to experimental and control groups and then intervene differently with each group. Group outcomes are then compared statistically to see if the outcomes between groups are significantly different from that would occur by chance alone. If the outcomes are significantly different, then we conclude that our intervention created the difference. Again, not a single RCT could be found about SC. Also missing are studies that are like RCTs, except that they lack either a control group, random assignment to groups, or both.
Correlation studies are next strongest. Now here we begin to find SC research! Correlation studies establish relationships between variables without being able to tell us whether one variable is causing another. Cyr (2002) found that gender was not related to beliefs in SC. Cyr's data also suggested that the HOHO score predicted presence of SC beliefs. The HOHO score is “the age at which parents stopped believing in Santa summed with the children's scores on the tooth fairy and Easter bunny questions to give a final score (HOHO score) of family fantasy predisposition” (Cyr, 2002, p. 1326).
We also have two descriptive studies about SC. One is a case study, an eyewitness account, that was poetically reported by Moore (1822) in Twas the Night Before Christmas. In the second, Anderson and Prentice (1994) observed that children often stop believing in SC at about 7 years of age and this makes children happy and parents sad. These descriptive studies are even less strong scientifically than correlation studies because they just describe things as they are. Descriptive research makes no effort at all to establish whether one thing causes another or even whether relationships exist between variables.
Finally we have an abundance of expert opinion evidence about SC. These are not research studies, and in this case the experts disagree with one another. Although over 100 years of consensus suggests that SC is a good thing (Breen, 2004; Elder, 1954; Nelms, 1996; Newseum, n.d.), a few authors argue otherwise. Occasional experts suggest that SC may cause or have problems like delivering unsafe toys (Thibedeau, 2009) or being associated with diabetes (Igoe, 2010). Related opinions are that SC impersonators may have positive (Burley, 1942) or negative (Collins, 2007) effects on society.
We could rate the quality of the evidence and place all this information in an Emergency Nurses Association (2011) evidence-appraisal template format and then perform critical appraisal using the critique worksheet. Using these tools, we may conclude that some scientific evidence suggests that SC might indeed be coming to town with a few possible caveats about health and safety.
USING THE EVIDENCE
Stetler (1994, 2001) suggests that we consider our best evidence in context, including the three Rs (i.e., resources, risks, and readiness), as well as its fit with our setting, current practices, and any corroborating sources. If the risks of a practice are high then the evidence strength should be high as well. In this case, the SC scientific evidence is weak, but we could still use it in practice because potential risks are low and benefit, high. The worst that may happen is possibly unsafe toys that could be discarded. The best? A stack of presents!
Existing SC evidence also fits with December practices of many, but not all (Hoff, 2008). Adequate resources exist, including frequent baking, piles of gift wrap, Christmas tree lots, cold weather, and stores filled with goodies. Furthermore, many of us, along with the experts, are quite ready to believe in Santa. Substantiating evidence includes many church traditions and authorities that honor SC, perhaps by his Saint Nicolas moniker.
This is enough for me to translate the evidence into my practice of hanging stockings and putting up a Christmas tree before December 25 each year. My evaluation of outcomes over half a century are that stockings fill up and the gifts under the tree proliferate overnight on Christmas Eve, and I haven't had negative outcomes. My practice creates memorable, measurable, positive outcomes without any threat to safety.
CREATING NEW EVIDENCE
Of course, my translation of existing SC evidence into practice would be strengthened by creating new SC knowledge via research. In the meantime I would advocate continuing low-risk SC practices. I'm personally a big believer in Santa and that he is active in visiting nice children (as opposed to naughty). My belief in Santa is legitimate “knowledge not yet modified by an encounter with new research evidence” (Wall, 2008, p. 48).
So if any of you out there are interested in creating new Santa knowledge through research then we are ready! Our global community's combined knowledge of “How to Get Evidence of Santa Claus” is growing at: http://www.wikihow.com/Get-Evidence-of-Santa-Claus.
Here are my suggestions on how to start with this topic. First, go to the wikihow site and local library to update yourself about the current state of knowledge on SC. Then you can design one of these three general types of studies to create new evidence.
First, in descriptive research you would simply observe and thoroughly document events and objects. You would not change anything and would just watch. For example, you might answer questions like what does SC look like, when does he show up, and are there really eight reindeer? Stake out the chimney or the roof for your answers.
Your detailed descriptions in these studies might use words only (qualitative research) or numbers only (quantitative research) or both words and numbers to describe something (mixed methods). Qualitative data might be a detailed description of Santa's outfit in the same way that newspaper editors describe brides' dresses. For example, “Santa wears a red satin mid-thigh length jacket trimmed in fake white rabbit fur.” Quantitative data might include counting the reindeer, the presents, or the length in inches of Santa's beard.
Once you get some of these events or things (variables) documented, try some correlation research in which you look for relationships among variables. For example, you might ask, “Is Santa more or less likely to appear when it snows?” You would decide how to measure snowing or not and then count the times Santa appears with and without the snow. In correlation research you would never say that the snow caused SC to appear, but would statistically decide only whether SC and snowing seem to happen (or not) at the same time more often than random chance.
After establishing a relationship between SC and snow, you might conduct an experiment comparing the homes of nice children who put out cookies and milk with the homes of those who do not put out cookies and milk when it is snowing on Christmas Eve (see Table 1 for the PICOT format per Emergency Nurses Association). We would call such a study a quasi-experiment rather than an experiment because no human subjects review committee would approve randomizing children to various homes around the country. So we would have to study nice children where they naturally occur. A true experiment, often called an RCT, would require that we randomize children to experimental and control groups and have an intervention. We would hold Christmas Eve snow constant.
Your hypothesis for this quasi-experiment might be that “Nice children who put out cookies and milk are more likely to receive SC home visits than nice children who do not put out the cookies and milk.” Like all good hypotheses, this one predicts what the answer is likely to be between clearly defined groups.
You will have to decide how you will measure the variable of cookies and milk. How many cookies? What type? How much milk in what kind of glass? The cookies and milk are called the independent (or cause) variable because we suspect this is what will create change in the dependent (or outcome) variable of SC home visits. You could measure SC home visits by whether they are present or absent or based on the length in minutes of the visit.
If we wanted to study a group of naughty children, too, then we would also define nice and naughty in measurable terms. For example, what are the numbers of bad or good behaviors in a year demonstrated by each child? Nice could be those that had over 75% good behaviors and naughty those with 75% bad behaviors. Of course, we would also need to control extraneous variables like naughty/nice parents, socioeconomic status, and so on. How your variables are measured is up to the researchers' good judgment, and it can actually affect the results of your study, so this is important. For example, measuring blood pressure with an arterial line may be more accurate than measuring it with an external cuff.
Our null hypothesis of course would be that SC equally visits both types of nice children. We would test that statistical (null) hypothesis by doing the math to see if the milk/cookies group got more visits than the no milk/cookies group at the p < 0.05 level. That p is less than 0.05 means that there is a 95% chance that SC really does visit the milk/cookies kids' homes more with a 5% chance that we are wrong, and he actually visits both homes equally. Alas, all these statistics are only probabilities and never sure answers. That's why studies have to be repeated many times under the same conditions; if the results continue to be the same, then we assume that it is more likely that our results reflect reality! To demonstrate that SC exists and acts to reward nice children who leave milk and cookies, we would have to repeat this study quite a few times.
Of course, before conducting any of these studies you must get the approval of your local human subjects review board (also called an institutional review board) to which the U.S. government has given the job of protecting subjects. They can help you decide whether and what consent from subjects is required. Studies of under-age children usually require parental consent.
Although all this evidence can enrich our SC-related practices, it is still true that in the end we may fare better simply to believe that Santa Claus is out there. Much authentic, valid nursing knowledge rests in our beliefs about the natural world when there is little or no research evidence (Wall, 2008). In the case of SC, such belief may be best expressed by F.P. Church, editor of The New York Sun, in 1897 who wrote: “The most real things in the world are those that neither children nor men can see” (as cited in Newseum, n.d.).
Anderson C. J., Prentice N. M. (1994). Encounter with reality: Children's reactions on discovering the Santa Claus myth. Child Psychiatry and Human Development, 25, 67–84. doi:10.1007/BF02253287
Breen L. (2004). What if Santa died? Childhood myths and development. The Psychiatrist, 28, 455–456. doi:10.1192/pb.28.12.455
Burley A. (1942, July 1). Santa Claus. RN, 54. Retrieved from EBSCOhost
Collins L. (2007, October/November). The “Kris Kringle” incident: Santa Claus he wasn't! Firefighters team up to rescue man trapped in chimney. Advanced Rescue Technology, 10(5), 8–10.
Cyr C. (2002). Do reindeer and children know something that we don't? Pediatric inpatients' belief in Santa Claus. Canadian Medical Association Journal, 167, 1325–1327.
Elder F. (1954, December). Santa Claus–“real” or “pretend”? RN, 17, 57. Retrieved from EBSCOhost
ENA Clinical Practice Guidelines Committee. (2011). Guidelines for the development of evidence-based emergency nursing resources. Retrieved from http://www.ena.org/IENR/ENR/Pages/Default.aspx
Igoe U. (2010). Santa Claus has diabetes. Podiatry Now, 13(12), 42.
Nelms B. (1996). Santa Claus: Good or bad for children? Journal of Pediatric Health Care, 10, 243–244.
Newseum. (n.d.). Yes, Virginia. There is a Santa Claus. Retrieved from http://www.newseum.org/yesvirginia/
Stetler C. B. (1994). Refinements of the Stetler/Marram model for application of research findings to practice. Nursing Outlook, 42, 15–25.
Stetler C. B. (2001). Updating the Stetler model of research utilization to facilitate evidence based practice. Nursing Outlook, 49, 272–279. doi:10.1067/mno.2001.120517
Thibedeau H. (2009). Safer toys coming, but not with Santa Claus. Canadian Medical Association Journal, 181(6/7), E111–E112. doi:10.1503/cmaj.109-3003
Wall S. (2008). A critique of evidence-based practice in nursing: Challenging the assumptions. Social Theory & Health, 6, 37–53. doi:10.1057/palgrave.sth.8700113
belief; evidence-based practice; research; Santa Claus
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