If you have a love for children, a creative personality and a lot of love to give, you may be perfectly suited for life as a nanny! There are many different types of nannies or au pairs. However, most excellent nannies have a handful qualities in common. Here we will address the most important qualities that every nanny should embody along with a few considerations a future nanny should take.
Not all nannies work full-time. But even just a few hours a week with the same child or children can be both challenging and rewarding! If you are willing and prepared to open up your heart to a new family several hours a week and patiently care for children even through the difficult times, you might just make an excellent nanny. You will find yourself kissing scraped knees and sometimes even breaking up fights between siblings. This will require a lot of compassion and patience!
Being a nanny is not for the faint of heart and it is in no way a passive job. You will need to be prepared to work constantly. You will need to find creative distractions and games and you will need to be willing to tutor young children through their homework. Nannies don’t just care for children, they also teach them essential life skills they need to care for themselves. You will likely spend a lot of your time as a nanny changing meeting the hygienic needs of children (dirty diapers anyone?) while also attending to household needs like laundry and cooking etc… the work is truly endless!
Becoming CPR certified is a skill a nanny hopes to never call on. But you and the parents you work for will have much greater peace of mind if you add CPR certification to your nanny-arsenal! Classes are simple and often times can be completed within just a day. CPR recommendations change somewhat regularly as our knowledge and understanding of the human anatomy deepens, so it is important to keep your certification current – don’t let it lapse! Nannies who are CPR certified can expect higher salaries, greater peace of mind and their choice of families to work for!
You don’t have to be tough on children to be disciplined. Children are funny little creatures that crave routine! As boring as it may seem to you, children may get into interesting little ruts that involve the same lunches or snacks every day, the same bedtime story, the same lullaby… But beyond their own individual quirks, they need a consistent schedule. If bath time is at 7:30 every evening, you need to strive to keep it that way no matter what. Make sure they are in bed at the same time nightly and waking up the same time each morning. This will require a lot of discipline on the rough days! In conclusion, being excellent takes a lot of heart, patience, and intelligence. Becoming a nanny can be a rewarding, diverse and fun career for the right person. The post Becoming an Excellent Nanny appeared first on . via Blogger Becoming an Excellent Nanny
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If you have a love for children, a creative personality and a lot of love to give, you may be perfectly suited for life as a nanny! There are many different types of nannies or au pairs. However, most excellent nannies have a handful qualities in common. Here we will address the most important qualities that every nanny should embody along with a few considerations a future nanny should take.
Not all nannies work full-time. But even just a few hours a week with the same child or children can be both challenging and rewarding! If you are willing and prepared to open up your heart to a new family several hours a week and patiently care for children even through the difficult times, you might just make an excellent nanny. You will find yourself kissing scraped knees and sometimes even breaking up fights between siblings. This will require a lot of compassion and patience!
Being a nanny is not for the faint of heart and it is in no way a passive job. You will need to be prepared to work constantly. You will need to find creative distractions and games and you will need to be willing to tutor young children through their homework. Nannies don’t just care for children, they also teach them essential life skills they need to care for themselves. You will likely spend a lot of your time as a nanny changing meeting the hygienic needs of children (dirty diapers anyone?) while also attending to household needs like laundry and cooking etc… the work is truly endless!
Becoming CPR certified is a skill a nanny hopes to never call on. But you and the parents you work for will have much greater peace of mind if you add CPR certification to your nanny-arsenal! Classes are simple and often times can be completed within just a day. CPR recommendations change somewhat regularly as our knowledge and understanding of the human anatomy deepens, so it is important to keep your certification current – don’t let it lapse! Nannies who are CPR certified can expect higher salaries, greater peace of mind and their choice of families to work for!
You don’t have to be tough on children to be disciplined. Children are funny little creatures that crave routine! As boring as it may seem to you, children may get into interesting little ruts that involve the same lunches or snacks every day, the same bedtime story, the same lullaby… But beyond their own individual quirks, they need a consistent schedule. If bath time is at 7:30 every evening, you need to strive to keep it that way no matter what. Make sure they are in bed at the same time nightly and waking up the same time each morning. This will require a lot of discipline on the rough days! In conclusion, being excellent takes a lot of heart, patience, and intelligence. Becoming a nanny can be a rewarding, diverse and fun career for the right person. The post Becoming an Excellent Nanny appeared first on . via Tumblr Becoming an Excellent Nanny Imagine you’re reading a study and you see that the participants were randomly allocated to two treatment groups. One group is allocated to receive surgery and the other to receive a drug intervention. Great, you think, they’ve been randomly allocated: this helps ensure the groups have similar characteristics. But it doesn’t stop there. For various reasons, participants may not actually receive or take their allocated treatment. A participant may choose not to take the treatment for example. It’s important that – as far as possible – all the patients allocated to these two groups are followed up and included in the main analysis of the group to which they were allocated. Even if – and this might initially seem counterintuitive – they never actually received the intervention they were allocated. This is because the characteristics of those who do not receive or take their treatment may differ from those who do. Consequently, excluding those who did not take their allocated treatment from the analysis may mean that your comparison is no longer fair. That is, you are no longer comparing like with like. Including participants in the analysis irrespective of whether they actually receive their allocated treatment is known as ‘intention-to-treat’ (ITT) analysisFisher et al. (1990) explain: “The ITT analysis includes all randomized patients in the groups to which they were randomly assigned, regardless of their adherence with the entry criteria, regardless of the treatment they actually received, and regardless of subsequent withdrawal from treatment or deviation from the protocol” Intention-to-treat analysis is usually described as “once randomized, always analyzed”For instance, in a comparison of surgery and drug treatments, people who die while waiting for their surgery should be counted in the surgery group, even though they didn’t actually receive the surgery. If they are not, you’re not actually comparing like with like. Intention-to-treat analysis avoids overestimating the efficacy and/or safety of an intervention which could result from ignoring those who never receive the treatment. This is because it accepts that non-compliance and non-adherence are likely to occur in actual clinical practice. That is, the effectiveness of a treatment is not simply determined by its actual biological effects. It’s also influenced by the patient’s ability to adhere to, and/or the clinician’s ability to administer, the intended treatment. Only by retaining all patients intended to receive a given treatment in their original treatment group can researchers and practitioners obtain an unbiased estimate of the effect of selecting one treatment over another. Let’s take an exampleImagine you’re comparing two intensities of cancer treatments (a high intensity and a low intensity). In the high intensity group, 10 of 50 participants choose not to take their treatment due to the unpleasant side effects of the treatment. In the low intensity group, the side effects are less extreme and only 3 of 50 participants choose not to take their treatment. Imagine then if you were only comparing the 40 patients who continued to take the high intensity treatment with the 47 who continued to take the treatment in the low intensity group. This would be misleading, because you’d be failing to take into account the tolerability of the treatment, when the tolerability of a treatment is very important factor! Let’s take another exampleThe figure, and example below, help to further illustrate why it’s important to carry out intention-to-treat analysis: Patients with partial blockages of blood vessels supplying the brain and who experience dizziness have an above average risk of having a stroke. Researchers have investigated whether operating on these patients to unclog blood vessels would reduce subsequent strokes. They compared all patients allocated to the operation, regardless of whether or not they survived the surgery, with all those patients in the comparison group, who did not receive surgery. If they’d recorded the frequency of stroke only among those patients who’d survived the immediate effects of the surgery, they would have missed the important fact that the surgery itself can cause stroke and death. So – if all other things were equal – the surviving participants in this group would have fewer strokes. That would be an unfair comparison because you need to consider the risks of the surgery itself. As shown in the figure above, the outcomes (i.e. mortality rate) of the surgery vs. the medical treatment are actually equal. But if the 2 people allocated to the surgery who had died before they actually received surgery were ignored (i.e. excluded from the analysis), the comparison of the 2 groups would have been biased. The results would indicate that surgery is superior to the comparator treatment when actually this is not the case. So what does this all mean for us, when we’re reading a study or a review?Unfortunately, researchers are often bad at applying intention-to-treat analysis and/or journals often poorly report on whether this analysis has been carried out. But it’s really important to be wary of, and cautious about, relying on the results of treatment comparisons if participants’ outcomes are not counted in the group to which they were allocated. Remember, best practice is: “once randomized, always analyzed”. ReferencesClick here for more resources explaining why people’s outcomes should be analyzed in their original groupsRead the rest of the blogs in the series hereTake home message:
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//platform.twitter.com/widgets.js via Tumblr https://t.co/9gdj1QFG2T Millennial women are more likely than men to say they have already reached a milestone they said represented financial adulthood. The post When Do You Become an Adult? appeared first on Earnest Blog | Money Advice for Young Professionals. via Blogger When Do You Become an Adult? Millennial women are more likely than men to say they have already reached a milestone they said represented financial adulthood. The post When Do You Become an Adult? appeared first on Earnest Blog | Money Advice for Young Professionals. via Tumblr When Do You Become an Adult? OverviewThe sample size is the proportion of the general population that are taking part in the study. However, it is crucial that the sample chosen is representative of the wider population, so that any conclusions drawn from the study can be reasonably extrapolated to individuals who did not directly take part. Sample sizeThere are a number of different methods for calculating sample size, with many alternate factors that influence these. Nevertheless, the common features include:
Significance levelA significance (alpha) level of P < 0.05 is universally accepted in most research fields. This means that there is a less than 5% chance that the obtained result of the study is due to chance alone. PowerThe power of a study is defined as its likelihood of obtaining the true outcome. It is calculated as: Power = 1 – β error (type 2 error) A type 2 error is failing to detect a significant difference when there actually is one. This usually occurs when the sample size is too small. In other words, power is the ability to avoid a false negative result, i.e. correctly rejecting the null hypothesis when it is actually false. The higher the power level the better, however, a power level of 80% is generally acceptable in most clinical research studies. Other areas of research will have different standards of power levels. Predicted effect sizeThe statistics involved in calculating a specific sample size can become quite complex. Part of this calculation involves inputting a ‘predicted effect size’. The smaller the predicted effect size you wish to obtain, the larger the sample must be. It is vital that the predicted effect size used in the calculation is properly justified. As this figure is user-generated, it can be manipulated by researchers to achieve a sample size figure that they desire. To combat this, predicted effect sizes should be based on a multitude of sources, including previous high quality evidence and clinical experience. Additional considerationsIt is also important to consider the expected drop-out or death rates in the relevant research field. Therefore, the calculation will produce a buffer of extra participants to account for any inevitable loss of numbers. Again, the exact drop-out and/or death rates should be based on previous studies. Sample size calculations must always be performed before the start of the study to eliminate any bias or deviation from study protocols. Another factor to consider is also practicality. In the real world sample sizes are, more often than not, influenced by administration limitations, costs as well as available resources. Why should I bother calculating my sample size?One of the main reasons that studies should be powered, and sample sizes should be calculated is ethics. It is unethical to subject patients to a particular experimental intervention if the study was not adequately powered to be able to detect any significant difference in the first place. Not only does this expose the participants to potential unnecessary harm, it is a significant detriment to both costs and resources. Summary and links to power calculatorsSample size calculations are necessary for any well designed clinical study. The implementation of these calculations should be based on acceptable alpha levels, power and a justified effect size. Calculating this figure by hand can be quite arduous for the average researcher. As a result, there are a number of online calculators that can be useful. Below are some links: http://www.raosoft.com/samplesize.html http://www.danielsoper.com/statcalc/category.aspx?id=19 https://www.surveymonkey.co.uk/mp/sample-size-calculator/ http://www.nss.gov.au/nss/home.nsf/pages/Sample+size+calculator References
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The post Sample size: A practical introduction appeared first on Students 4 Best Evidence. via Blogger Sample size: A practical introduction OverviewThe sample size is the proportion of the general population that are taking part in the study. However, it is crucial that the sample chosen is representative of the wider population, so that any conclusions drawn from the study can be reasonably extrapolated to individuals who did not directly take part. Sample sizeThere are a number of different methods for calculating sample size, with many alternate factors that influence these. Nevertheless, the common features include:
Significance levelA significance (alpha) level of P < 0.05 is universally accepted in most research fields. This means that there is a less than 5% chance that the obtained result of the study is due to chance alone. PowerThe power of a study is defined as its likelihood of obtaining the true outcome. It is calculated as: Power = 1 – β error (type 2 error) A type 2 error is failing to detect a significant difference when there actually is one. This usually occurs when the sample size is too small. In other words, power is the ability to avoid a false negative result, i.e. correctly rejecting the null hypothesis when it is actually false. The higher the power level the better, however, a power level of 80% is generally acceptable in most clinical research studies. Other areas of research will have different standards of power levels. Predicted effect sizeThe statistics involved in calculating a specific sample size can become quite complex. Part of this calculation involves inputting a ‘predicted effect size’. The smaller the predicted effect size you wish to obtain, the larger the sample must be. It is vital that the predicted effect size used in the calculation is properly justified. As this figure is user-generated, it can be manipulated by researchers to achieve a sample size figure that they desire. To combat this, predicted effect sizes should be based on a multitude of sources, including previous high quality evidence and clinical experience. Additional considerationsIt is also important to consider the expected drop-out or death rates in the relevant research field. Therefore, the calculation will produce a buffer of extra participants to account for any inevitable loss of numbers. Again, the exact drop-out and/or death rates should be based on previous studies. Sample size calculations must always be performed before the start of the study to eliminate any bias or deviation from study protocols. Another factor to consider is also practicality. In the real world sample sizes are, more often than not, influenced by administration limitations, costs as well as available resources. Why should I bother calculating my sample size?One of the main reasons that studies should be powered, and sample sizes should be calculated is ethics. It is unethical to subject patients to a particular experimental intervention if the study was not adequately powered to be able to detect any significant difference in the first place. Not only does this expose the participants to potential unnecessary harm, it is a significant detriment to both costs and resources. Summary and links to power calculatorsSample size calculations are necessary for any well designed clinical study. The implementation of these calculations should be based on acceptable alpha levels, power and a justified effect size. Calculating this figure by hand can be quite arduous for the average researcher. As a result, there are a number of online calculators that can be useful. Below are some links: http://www.raosoft.com/samplesize.html http://www.danielsoper.com/statcalc/category.aspx?id=19 https://www.surveymonkey.co.uk/mp/sample-size-calculator/ http://www.nss.gov.au/nss/home.nsf/pages/Sample+size+calculator References
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AuthorMy favorite subject is chemistry and math. My goal is to be a good model to all students and to the next generation. I am also a member of our school's basketball team. ArchivesCategories |