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Topic 1: Evidence-Based Practice

Illustrate the 5 As the process to EBP

ANS: The 5 A’s process is a framework that can help guide the implementation of EBP in clinical practice. These are:

  1. Assess: Start with the patient to determine a clinical problem or question that arises from the care of the patient.
  2. Ask: Construct a well-built question derived from the case.
  3. Acquire: Select the appropriate resources and conduct a search.
  4. Appraise: Appraise that evidence for its validity and applicability.
  5. Apply: Return to the patient, integrate the evidence with clinical expertise and patient preferences, and apply it to practice.

Create each of these types of questions for your profession:

  • Prevention
  • Screening
  • Diagnosis
  • Prognosis
  • Aetiology
  • Therapy
  • Patient/client experiences/concerns

ANS:

  1. Prevention: What strategies are most effective in preventing healthcare-associated infections in hospital settings?
  2. Screening: What are the most reliable screening tool for early detection of cognitive decline in older adults?
  3. Diagnosis: Can wearable devices aid in the early diagnosis of cardiovascular conditions in at-risk patients?
  4. Prognosis: What factors contribute to the long-term prognosis and quality of life of cancer survivors?
  5. Etiology: What is the role of dietary and lifestyle factors in the etiology of type 2 diabetes?
  6. Therapy: How effective are mindfulness-based interventions in reducing symptoms of anxiety and depression in adolescents?
  7. Concerns: What are the primary concerns and information needs of patients when making decisions about end-of-life care?

Formulate a PICO question and your search strategy to find the best available evidence.

PICO question: Does regular aerobic exercise compared to a sedentary lifestyle (comparison) and reduce the risk of developing cardiovascular disease (outcome) among older adults above 65 years (population).

Search strategy:

The first step of the search is to identify keywords such as older adults, aerobic exercise, sedentary lifestyle and cardiovascular disease. The second step in the search strategy is to use the Boolean operator. By using the selected keywords, search published studies from reliable online data sources. Apply filters like clinical trials, systematic review, or randomized controlled trials. Review the search results for relevance and quality of the studies. Access the full text to evaluate their quality and relevance to the clinical question.

Topic 2 Causality and Randomised Trials

Describe the importance of causality in Evidence-Based Practice

Causality is helpful in establishing a direct and meaningful relationship between interventions or factors and their effects on patient outcomes. Understanding causality enables practitioners to make informed decisions based on the likelihood of specific interventions leading to desired outcomes (Tellings, 2017).

Illustrate the key elements of a randomized controlled trial and how bias can be introduced at key points.

RCT is a rigorous experimental design used in research to investigate the effects of an intervention or treatment. Some of the key elements of RCT are randomization, control group, blinding, and outcome measures. In RCT, participants are randomly assigned to treatment groups. The control group received a placebo or standard treatment for comparison with the treatment group. Blinding involves keeping participants, healthcare providers, and assessors unaware of the treatment allocation to prevent performance bias and detection bias can occur. If the control group is not well-matched to the treatment group or if the control group receives a treatment that is not a true placebo, bias can be introduced.

Describe and provide an example of all probability and non-probability sampling types used in health science research.

Probability sampling:

  1. Simple random sampling: In simple random sampling, every member of the population has an equal chance of being selected.
  2. Stratified sampling: The population is divided into subgroups or strata based on certain characteristics.
  3. Systematic sampling: Researchers select every nth member of the population for the sample. The first member is selected randomly.
  4. Cluster sampling: The population is divided into clusters or groups and a random sample of clusters is selected.

Non-probability sampling:

  1. Convenience sampling: Researcher select participants based on their convenience or accessibility. It may not be representative of the entire population.
  2. Purposive sampling: In its participants are selected who meet specific criteria or characteristics relevant to the study’s objectives.
  3. Snowball sampling: This method is often used in hard-to-reach populations.
  4. Quota sampling: Researchers set quotas for various demographic or other characteristics and then purposefully sample individuals to fill those quotas.
  1. Purposive sampling: Researchers intentionally select participants

Topic 3 How can we trust Randomised Trials?

Describe selection bias, performance bias, detection bias, and attrition bias.

Selection bias: It occurs when there is a systematic difference between the individuals or groups selected for a study and the broader population from which they are drawn.

Performance bias: It arises when there are systematic differences in the care provided to different groups in a study. Performance bias occurs when participants or healthcare providers are aware of the treatment they are receiving, leading to variation in care (Mokkink et al., 2023).

Detection bias: It occurs when there are systematic differences in the assessment or measurement of outcomes between groups. This can result from knowledge of the intervention, leading to bias in outcome assessment. 

Attrition bias: It occurs when there is a systematic difference between participants who remain in a study and those who drop out or are lost to follow-up. This can affect the generalizable of study findings and lead to a biased estimates of treatment effects (Mokkink et al., 2023).

Define issues of validity and reliability in research 

Both validity and reliability are the basic properties of empirical measurements. Reliability can be defined as the degree to which research instruments, procedures or measurements yield to assess the extent to which the research findings are replicable. Whereas validity is the degree to which an instrument measures what it purports to measure ((Mansournia et al., 2018).

Identify sources of measurement error

Ans: Some of the sources of measurement error are instrumentation error, sampling error, time-related error, random variation, and bias. If the measuring instrument is not working accurately, then it can introduce errors into measurements. Small sample size or wrong selection of population can also result in measurement error. During data collection, if temporal changes are made, like taking measurements at different times of day under different conditions, it can lead to time-related errors (Mokkink et al., 2023).

Topic 4 Interpretation of Results 1

Define the different types of data and provide an example for each

The data is classified into four categories:

  1. Nominal data: it is used to label variables without any order or quantitative values.

Example: the colour of hair (Blonde, red, brown, black, etc.)

  1. Ordinal data: it represents categories or labels with a meaningful order or ranking. Ordinal data is qualitative data for which their values have some kind of relative position.

Example: Ranking of people in a competition (High, Medium, and Low).

  1. Discrete data: The term discrete is used for distinct or separate. This type of data is countable and have finite values, their subdivision is not possible.

Example: total number of students present in a class.

  1. Continuous data: This type of data is present in form of fractional numbers. continuous data stores the fractional numbers to record different data types such as temperature, height, width, time and, speed, etc.

Example: Height of a person.

Define a 95% confidence interval. Why do we prefer to use 95% confidence intervals over p values?

A 95% confidence interval (CI) is a statistical range or interval constructed around a sample estimate to provide a range of values within which the true population parameter is likely to fall with a 95% level of confidence. 95% confidence intervals are preferred over p values as it also give an estimate of the magnitude and precision of the effect. CI provides more transparency in results and making it easier to assess the robustness and reliability of the findings (Du Prel et al., 2019).

Write an example of a null hypothesis and its accompanying alternative hypothesis.

According to the null hypothesis, there is no significant difference in the mean test scores between students who are involved in traditional teaching methods and students who are involved in online teaching methods in physics courses. However, according to the alternative hypothesis, there is a significant difference in the mean test scores between students who receive a traditional teaching method and students who receive an online teaching method for a mathematics course. In this example, the researcher has to perform a study to collect the evidence which can reject the null hypothesis. If the researcher sucked to provide strong evidence, they can conclude that the teaching method has a significant impact on test scores.

Topic 5 Interpretation of Results 2

Describe what are:

  • Confidence intervals
  • Mean Difference
  1. Confidence interval: It is a statistical concept used in data analysis and inferential statistics. It shows the probability that a parameter will fall between a pair of values around the mean. Confidence interval show the degree of uncertainty or certainty in a sampling method. Confidence intervals are calculated on the basis of sample data. Researchers collect a sample from a population and then use statistical methods to estimate the range of values that the population parameter is likely to fall into.
  2. Mean difference: Mean difference can be defined as a statistical measure that quantifies the average difference between two sets of values, typically sample means, population means, or treatment effects (Andrade et al., 2020). The mean difference is generally used in hypothesis testing and significance testing to determine if the difference between the two groups is statistically significant or if it could have occurred by chance.

Define relative risk (RR) and odds ratio (OR)

Relative risk (RR) is a ratio of the probability of an event occurring in the exposed group versus the probability of the event occurring in the non-exposed group. RR measured the likelihood of an event occurring in one group compared to another group (Tenny & Hoffman, 2017). 

The odds ratio (OR) is a measure of the association between an exposure and an outcome. It compares the odds of an event occurring in the exposed group to the odds of the event occurring in the unexposed group (Tenny & Hoffman, 2017). 

Describe why the minimum clinically important difference is essential when interpreting results.

The minimal clinically significant difference (MCID) denotes the smallest improvement considered worthwhile by a patient. Or it represents the smallest change in a clinical outcome that is considered meaningful and relevant to patients or healthcare providers (Mouelhi et al., 2020). MCID is considered important while interpreting result as it is helpful to determine whether a statistically significant change is clinically meaningful. With the help of MCID, the effectiveness of healthcare intervention can be evaluated and help the healthcare professional to inform decisions about resource allocation and treatment recommendations (Mouelhi et al., 2020).

Topic 6 Ethics

Qn 1

Use an example of unethical healthcare or research on Aboriginal and Torres Strait Island peoples and reflect on what ethical principles were broken. 

One of the unethical practices involving Aboriginals is the Stolen Generations. This policy resulted in the forced removal of Indigenous children from their families and communities and their placement in institutions or with non-Indigenous families. This policy leads to the violation of several ethical principles, including autonomy and informed consent, beneficence, non-maleficence, and justice and equity.

Search for the Curtin Human Research Ethics and find the National Statement on Ethical Conduct in Research 2007 (updated 2018). Show a screenshot that you have seen both and know how to access it.

Please add a screenshot from your own search history it will be more convenient.

Using this document from the NHMRC: Ethical conduct in research with Aboriginal and Torres Strait Islander Peoples and communities | NHMRC write and describe the 6 core values.

The six core values are:

  1. Spirit and integrity: This core value identifies the spiritual and cultural significance of Indigenous knowledge and traditions. Spirit refers to the ongoing connection and continuity between Aboriginal's past, current, and future generations. Whereas integrity denotes the respectful and honourable behaviours that identify Aboriginal's values and cultures together.
  2. Cultural continuity: It maintains the bonds and relationships between people and their environment. It acknowledges the importance of maintaining and respecting the traditions, customs, and heritage of indigenous communities.
  3. Equity: It emphasizes the need for fairness and justice in research involving Indigenous communities.
  4. Respect: Researchers must engage respectfully with cultural protocols and community preferences. It involves treating Aboriginals with dignity, recognizing their rights, and valuing their contributions and perspectives,
  5. Reciprocity: It reflects the importance of giving back to indigenous communities and fostering mutually beneficial relationships.
  6. Responsibility: It underscores the ethical obligations of researchers to conduct research that is safe, culturally appropriate, and responsive to community needs and priorities.

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