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To examine health professionals’ views and practices relating to the specific barriers to communication that arise at the time of mental health diagnosis, and the strategies used to support individuals throughout this process.
An online survey of the beliefs and practices of 131 mental health clinicians working in different clinical settings across Australia was conducted.
Exploratory factor analysis of the items relating to barriers to communication resulted in three latent factors (‘stigma, diagnosis and risk’; ‘service structure’; and ‘individual circumstances’ such as the person receiving the diagnosis being young, having a culturally and linguistically diverse background or being unwell at the time of conversation). Using linear regression it was found that variance in ‘stigma, diagnosis and risk’ was significantly explained by whether participating clinicians had medical training, their experience working with serious mental health problems, their confidence handling distress and attitude towards diagnosis. Variance in ‘individual circumstances’ was significantly explained by participating clinicians’ confidence handling distress. The most frequently used strategies to support diagnostic discussions centred on the health professionals’ communication skills, gauging the individual's perception of their circumstances, responding with empathy, following-up after discussion, addressing stigma concerns, using collaborative practice and setting up for the conversation.
Three main areas for health professionals to reflect on, plan for and ultimately address when discussing news with the individual concerned emerged (‘stigma, diagnosis and risk’; ‘service structure’; and ‘individual circumstances’). Variations in practice indicate that practitioners should be cognisant of their own beliefs and background and how this impacts their communication practice.
The evolution of a tandem accelerator 14C dating system at Chalk River is recounted. Background problems and sources of instability are discussed and solutions are described. Details of sample chemistry and source preparation are presented.
Canadian deuterium uranium (CANDU) pressurized heavy-water reactors produce 14C by neutron activation of trace quantities of nitrogen in annular gas and reactor components (14N(n,p)14C), and from 17O in the heavy water moderator by (17O(n,α)14C). The radiocarbon produced in the moderator is removed on ion exchange resins incorporated in the water purification systems; however, a much smaller gaseous portion is vented from reactor stacks at activity levels considerably below 1% of permissible derived emission limits. Early measurements of the carbon speciation indicated that >90% of the 14C emitted was in the form of CO2. We conducted surveys of the atmospheric dispersion of 14CO2 at the Chalk River Laboratories and at the Pickering Nuclear Generating Station. We analyzed air, vegetation, soils and tree rings to add to the historical record of 14C emissions at these sites, and to gain an understanding of the relative importance of the various carbon pools that act as sources/sinks within the total 14C budget. Better model parameters than those currently available for calculating the dose to the critical group can be obtained in this manner. Global dose estimates may require the development of techniques for estimating emissions occurring outside the growing season.
The need for increased quality assurance for radiocarbon measurements performed by the monitoring laboratories at nuclear stations has spurred the introduction of a number of interlaboratory comparisons. We organized two such intercomparisons: the first set, circulated in 1994, consisted of two milk samples, one containing current global levels of 14C, the other containing an added spike of 14C-methylated casein. The second set, circulated in 1995, consisted of two samples of natural vegetation growing on the site of the Chalk River Laboratories (CRL), containing two different levels of 14C, both well above global background. The response to our invitation to participate in these studies was very encouraging; six laboratories took part in the first intercomparison, eleven in the second. The list included both monitoring laboratories and those whose main function is 14C dating. Understandably, some of the latter preferred not to analyze the higher-activity samples. The results in 3 of the 4 data sets were consistent with a statistical distribution based on the reported errors. This report provides details of two intercomparisons, including the preparation of the samples, which may now be considered potential secondary reference materials, the range of analytical techniques in use at the participating laboratories, and a statistical analysis of the results returned to us.
The Chalk River Tandem Accelerator Mass Spectrometry System has reached a state of reliable measurement of 14C using 2 to 5mg elemental carbon prepared by Mg reduction of CO2. For two comparisons of a near-modern unknown with the NBS oxalic acid standard we obtain a total error of ∼±4.5%, consisting of a random system error of about ±3.5% combined with the statistical counting error. Measurements have been made on 70 samples in 30 days of running time during the past year. Samples included deep rock carbonates, cosmogenic 14C in meteorites, charcoal from earthquake fault zones, collagen of bone artifacts and fossil beetle-fragments.
Chitosan is biocompatible polymer has a great commercial interest because it can be processed in a sort of devices varying in shape and size, such as membranes, gels and nanoparticles. Mostly, the cell’s attachment and proliferation are very positive on nanostructurated materials with a three-dimensional formation. An irreversible network can be produced by covalently binding the polymer to the cross-linker molecules. Chitosan nanoparticles were prepared using glutaraldehyde as cross-linker. This crosss-liker mostly reacts with chitosan amino groups. In order to control and understand the physical characteristics of chitosan nanoparticle, in this work is showed the molecular behavior of chitosan/glutaraldehyde from the viewpoint of molecular interactions base in a series of molecular dynamics (MD) computer simulation. The results indicated the conformations of both molecules, which had a significant influence on the molecular association. The chitosan chains were uniformly distributed presenting a high flexibility and preference for the relaxed two-fold helix. This was due to the various associations such as intramolecular chitosan interactions –O-H···O-C-. While the chitosan-glutaraldehyde associations were due to the positive net charge density of hydrogens in the chitosan plus - H2N···C=O associations. In solid state chitosan nano and microparticles were analyzed by scanning electron microscopy (SEM). According to the micrographs results, the nanoparticles presented a monomorphism with piles of particles arranged in linear order which was consistent with the conformations determined by simulation.
Carbon nanotubes (CNTs) were synthesized by Chemical Vapor Deposition (CVD) from diethyl ether, butanol, hexane and ethyl acetate. A quartz tube with a stainless steel tube catalyst core with 0.019 m diameter and 0.6 m large formed the reactor. To avoid combustion, argon was used as the carrier gas. Time process ranged 30 to 60 min. The range of CNTs synthesis temperature was 680-850 °C for different precursors. Scanning Electron Microscopy micrographs have demonstrated tangled CNTs growth in all samples, thus presenting difficult length measurement. The CNTs diameters from diethyl ether are 45-200 nm, butanol diameter range from 55-230 nm, hexane diameter range is 50-130 nm and ethyl acetate range from 100 to 300 nm. Carbon content for all samples was higher than 93 %, CNTs from butanol showed carbon concentration up to 99%. FTIR, Raman and X-Ray Spectroscopies spectra for all samples demonstrated the characteristics signals present in carbon nanotubes. This research proposes a simple, effective and innovative method to synthesize CNTs by CVD on iron stainless steel catalyst in combination with diethyl ether, ethyl acetate, butanol and hexane as precursors by applying the principles of green chemistry, sustainability and its ease to be scaled.
Development of polymers with antimicrobial characteristics can avoid deterioration and assist in containing spread of pathogens harmful to human health. This study aimed to compare the antimicrobial and mechanical properties of polymeric matrices containing organic antimicrobial additives. Silver organomodified bentonite (Ag_bentonite) and organochlorine molecule in a masterbatch based polyethylene (Cl_PE) were tested in proportion of 2% in a thermoplastic elastomeric formulation. The polymeric matrices were prepared by melt mixing and evaluated in tensile and antimicrobial properties against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) strains. The additives were characterized by thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The nanoscale of Ag_bentonite was verified by SEM. TGA assay showed that Cl_PE is more sensitive to heat than Ag_bentonite. As a result of this lower thermal stability, the addition of Cl_PE reduced the tensile properties of the compound. The sample with Cl_PE was effective against both bacterial strains, reducing the populations of S. aureus and E. coli in 99 and 96%, respectively. The addition of Ag_bentonite did not affect the tensile strength and decreased in 97 and 40% S. aureus and E. coli populations, respectively. The results indicate that the use of organic additives is promissory, but further modifications in processing must be necessary.
Given the capacity of ruminants to modify diet selection based on metabolic needs, we hypothesised that, when given a choice, lambs experiencing a vitamin E deficiency would consume more of a vitamin E-enriched feed than lambs not deficient in vitamin E. Fifty-six Dohne Merino lambs were divided into two groups and fed either a vitamin E-deficient diet over 40 days to induce low plasma vitamin E or a vitamin E-enriched diet to induce high plasma vitamin E. The lambs were then offered a choice of vitamin E-enriched and vitamin E-deficient pellets. For half of the animals, the enriched diet was paired with strawberry flavour and the deficient diet was paired with orange flavour, while the reverse pairings were offered to the others. Lamb preference for the diets was measured daily for the following 15 days. There was a three-way interaction between the high and low vitamin E treatment groups×vitamin E content and type of flavour in the feed×time (days). The lambs preferred pellets flavoured with strawberry but this preference changed to orange flavour in vitamin E-deficient lambs if the orange flavour was paired with high vitamin E. Lambs without a deficiency continued to prefer strawberry-flavoured pellets, regardless of the vitamin E concentrations in the pellets. It is possible that self-learning contributed to the low vitamin E group of lambs changing preference to orange flavour in order to consume more vitamin E, presumably to remediate the deficiency.
We report optically active ensembles of II-VI semiconductor nanocrystals prepared via chiral phase transfer, which is initiated by exchange of the original achiral ligands capping the nanocrystals surfaces for chiral L- and D-cysteine. We used this method to obtain ensembles of CdSe, CdS, ZnS:Mn, and CdSe/ZnS quantum dots and CdSe/CdS quantum rods exhibited Circular Dichroism (CD) and Circularly Polarized Luminescence (CPL) signals. The optically active nanocrystals revealed the CD and CPL bands strongly correlated with absorption and luminescence bands with unique band “pattern” for each material and the nanocrystal shape.
Medicine is a science of uncertainty and an art of probability.
Sir William Osler
Decision trees and Markov cohort models, as described and illustrated in the previous chapters, are essentially macrosimulation models. Such models simulate cohorts or groups of subjects. A number of limitations exist to the use of these models. Markov cohort models, for example, have ‘no memory’, implying that subjects in a particular state are a homogeneous group. Techniques to overcome these limitations, such as expanding the number of states, using tunnel states, or using alternative modeling techniques, were discussed in Chapter 10. These techniques can get very complex when dealing with extensive heterogeneity within a population. Microsimulation using Monte Carlo analysis provides another powerful technique to account for heterogeneity across subjects. Microsimulation with Monte Carlo analysis was introduced in Chapter 10 as an alternative method for evaluating a Markov model. In this chapter it will be discussed at greater length in the context of simulating heterogeneity.
In the previous chapters we represented uncertainty with probabilities. Implicitly the assumption was that, even though we were unsure of whether an event would take place, we could nevertheless predict or estimate the probability (or relative frequency) that it would occur. In essence we were using deterministic models. In reality, however, we are also uncertain of the degree of uncertainty. In other words, rather than dealing with a fixed probability we are actually dealing with a distribution of possible values of probabilities. Not only are we uncertain about the probabilities we use in our models, but we are also uncertain about the effectiveness outcomes and cost estimates included in the analysis. Thus, every parameter value we enter into our models is better represented as a probabilistic variable rather than a deterministic variable. If there is a single uncertain parameter, e.g., the relative risk reduction of an intervention, then the 95% confidence interval (CI) of this parameter is commonly used to indicate the uncertainty of the effect. Uncertainty in two or more components requires more complex methods, such as Monte Carlo probabilistic sensitivity analysis, which we will also discuss in this chapter.
Values are what we care about. As such, values should be the driving force for our decision making. They should be the basis for the time and effort we spend thinking about decisions. But this is not the way it is. It is not even close to the way it is.
Value judgments underlie virtually all clinical decisions. Sometimes the decision rests on a comparison of probability alone, such as the probability of surviving an acute episode of illness. In such cases, there is a single outcome measure – the probability of immediate survival – that can be averaged out to arrive at an optimal decision. In most cases, however, decisions between alternative strategies require not only estimates of the probabilities of the associated outcomes, but also value judgments about how to weigh the benefits versus the harms, and how to incorporate other factors like individual preferences for convenience, timing, who makes decisions, who else is affected by the decision, and the like. Consider the following examples.
The interpretation of new information depends on what was already known about the patient.
Diagnostic information and probability revision
Physicians have at their disposal an enormous variety of diagnostic information to guide them in decision making. Diagnostic information comes from talking to the patient (symptoms, such as pain, nausea, and breathlessness), examining the patient (signs, such as abdominal tenderness, fever, and blood pressure), and from diagnostic tests (such as blood tests, X-rays, and electrocardiograms (ECGs)) and screening tests (such as Papanicolaou smears for cervical cancer or cholesterol measurements).
Physicians are not the only ones that have to interpret diagnostic information. Public policy makers in health care are equally concerned with understanding the performance of diagnostic tests. If, for example, a policy maker is considering a screening program for lung cancer, he/she will need to understand the performance of the diagnostic tests that can detect lung cancer in an early phase of the disease. In public policy making, other types of ‘diagnostic tests’ may also be relevant. For example, a survey with a questionnaire in a population sample can be considered analogous to a diagnostic test. And performing a trial to determine the efficacy of a treatment is in fact a ‘test’ with the goal of getting more information about that treatment.
Some treatment decisions are straightforward. For example, what should be done for an elderly patient with a fractured hip? Inserting a metal pin has dramatically altered the management: instead of lying in bed for weeks or months waiting for the fracture to heal while blood clots and pneumonia threatened, the patient is now ambulatory within days. The risks of morbidity and mortality are both greatly reduced. However, many treatment decisions are complex. They involve uncertainties and trade-offs that need to be carefully weighed before choosing. Tragic outcomes may occur no matter which choice is made, and the best that can be done is to minimize the overall risks. Such decisions can be difficult and uncomfortable to make. For example, consider the following historical dilemma.
Benjamin Franklin and smallpox
Benjamin Franklin argued implicitly in favor of the application to individual patients of probabilities based on previous experience with similar groups of patients. Before Edward Jenner’s discovery in 1796 of cowpox vaccination for smallpox, it was known that immunity from smallpox could be achieved by a live smallpox inoculation, but the procedure entailed a risk of death. When a smallpox epidemic broke out in Boston in 1721, the physician Zabdiel Boylston consented, at the urging of the clergyman Cotton Mather, to inoculate several hundred citizens. Mather and Boylston reported their results (1):
Out of about ten thousand Bostonians, five thousand seven hundred fifty-nine took smallpox the natural way. Of these, eight hundred eighty-five died, or one in seven. Two hundred eighty-six took smallpox by inoculation. Of these, six died, or one in forty-seven.
Before ordering a test ask: What will you do if the test is positive? What will you do if the test is negative? If the answers are the same, then don’t do the test.
Poster in an Emergency Department
In the previous chapter we looked at how to interpret diagnostic information such as symptoms, signs, and diagnostic tests. Now we need to consider when such information is helpful in decision making. Even if they reduce uncertainty, tests are not always helpful. If used inappropriately to guide a decision, a test may mislead more than it leads. In general, performing a test to gain additional information is worthwhile only if two conditions hold: (1) at least one decision would change given some test result, and (2) the risk to the patient associated with the test is less than the expected benefit that would be gained from the subsequent change in decision. These conditions are most likely to be fulfilled when we are confronted with intermediate probabilities of the target disease, that is, when we are in a diagnostic ‘gray zone.’ Tests are least likely to be helpful either when we are so certain a patient has the target disease that the negative result of an imperfect test would not dissuade us from treating, or, conversely, when we are so certain that the patient does not have the target disease that a positive result of an imperfect test would not persuade us to treat. These concepts are illustrated in Figure 6.1, which divides the probability of a disease into three ranges:
do not treat (for the target disease) and do not test, because even a positive test would not persuade us to treat;
test, because the test will help with treatment decisions or with follow-up; and
treat and do not test, because even a negative test would not dissuade us from treating.
Treat implies patient management as if disease is present and may imply initiating medical therapy, performing a therapeutic procedure, advising a lifestyle or other adjuvant intervention, or a combination of these. Do not treat implies patient management as if disease is absent and usually means risk factor management, lifestyle advice, self-care and/or watchful waiting.
In previous chapters we have seen several applications of decision trees to solve clinical problems under conditions of uncertainty. Decision trees work well in analyzing chance events with limited recursion and a limited time horizon. The limited number of sequential decisions or chance nodes allows one to capture all the necessary information to maximize expected utility. However, when events can occur repeatedly over an extended time period, the decision-tree framework can become unmanageable. Many decision situations involve events occurring over the lifetime of the patient, thus extending far into the future. Life spans vary, but conventional trees require us to specify a fixed time horizon. The probabilities and utilities of these events may change over time and must be accounted for. This is the case for most chronic conditions. Examples include heart disease, Alzheimer’s disease, various cancers, diabetes, asthma, osteoporosis, human immunodeficiency virus (HIV), inflammatory bowel disease, multiple sclerosis and more. This chapter offers a methodology for dealing with recurring events and extended (variable) time horizons.
Consider a patient with peripheral arterial disease (PAD: obstruction of the arteries to the legs) for whom a decision has to be made for either bypass surgery or percutaneous intervention (PI). We assume that conservative treatment through an exercise regimen has not provided sufficient relief. A very simplified decision tree is presented in Figure 10.1. Following the choice of treatment, the patient may die as a result of the procedure (captured in the ‘mortality’ branches) or survive the procedure. If the patient survives, treatment may fail and the patient returns to the pre-procedure prognosis, or treatment may be successful and the patient is relieved of symptoms. If we consider some fixed time horizon like a year or five years, we can assign utilities to the three possible outcomes (success, failure, death) and calculate expected utilities to choose a preferred treatment. In the current structure, there is no explicit allowance for the time horizon we are considering, nor for the timing of the various events. Even if we consider a fixed time horizon of, say, five years, there surely is a different implication for prognosis if failure occurs in the first year versus the fifth year.