ALHT211: Week 1-5 Exam Practice Questions PDF

**Week 1**  **Weekly goal:** - Explain the importance of applying research and evidence in allied health practice - Identify relevant evidence-based practice (EBP) frameworks and processes - explain main components and importance of knowledge translation as a concept   The te...

**Week 1**  **Weekly goal:** - Explain the importance of applying research and evidence in allied health practice - Identify relevant evidence-based practice (EBP) frameworks and processes - explain main components and importance of knowledge translation as a concept   The terms \"**evidence-based**\" and \"**evidence-informed \'**are interchangeable.   **Why use evidence?** Research helps inform our clinical thinking   - Non-linear process= circular - Research to inform practice throughout all steps above   Strong clinical reasoning= better clinicians Quality Care!! Ethical care!!    **What is evidence?** **Is it just research?**   **Components of Evidence-informed Practice** Evidence-based/Evidence-informed practice - Clinical expertise - Peer-reviewed research - Patient/client needs, preferences - Service context- budget constraint etc.   **EBP/E^4^BP framework**        **The 5 steps of the EBP process**        - **P: problem/ patient, I: intervention/interest, C: comparison of treatments/ populations, O: outcome \> Used to help find information to help answer clinical question**   **To appraise research, we need to understand it..**   Research process.      - **Quantitative**= numbers- correlations, determine cause and effect, frequency, course/prognosis, populations and assumptions of data - **Qualitative**= quality/words/ themes-   **Critical Appraisal**   **CASP Critical Appraisal Checklist- Assessment 2**   - **Support the user in being systematic** - **Ensure all important factors/ considerations are taken into account** - **Increase consistency in decision making by providing a framework**     **Knowledge translation (KT)** "The bumpy ride from bench to bedside" Morgan, Hanna & Yousef (2020)   - Shows how knowledge translation is used in the field of oncology and genome testing - Practical- information and quotes - \" Knowledge translation aims to bridge the evidence practice gap, by developing, implementing and evaluating strategies designed to enhance awareness and promote behavioural change that's congruent with research evidence\"    **Knowledge translation** - Knowledge creation - Knowledge integration- using the people around you to integrate knowledge for better services (clinicians, public, consumers) - Implementation & dissemination (ACTION!)- got the knowledge and communicated effectively... NOW implementing the service   - Doesn't always work in practice   **Knowledge translation** **Implementation includes...** Government, health organisations, regulatory bodies Consumer engagement 'De-implementation' of outdated practices Increases sustainability ** Includes everybody**      - Five A\'s framework- ideal for appraisal and PICO question - Clinical reasoning cycle - EBP/E^4^BP - Knowledge translation     Research & Evidence- informed practice = Ethical, Current, Consumer-focused healthcare  **Week 2** **ETHICS IN PRACTICE**      Australian Occupational Therapy Competency Standards Professionalism Knowledge and learning Occupational therapy process Communication   Professional Standards for Speech Pathologists in Australia (PSSP) Professional Conduct Reflective practice and life-long learning Speech Pathology practice          **ETHICS IN RESEARCH** **Why do we need research ethics?** Protect participants -- human rights and dignity Protect society, public health History of poor research and cases of human rights abuse in research\*   Nazi Experiments in War The Monster study (1939) Wakefield Study (1998)   **Codes of Conduct in Ethical research** - Nuremberg Code (1949) - principles to uphold human rights in research - Declaration of Helsinki (1964)- World medical association: provided list of responsibilities for researches to abide by e.g. consent + confidentiality - "Statement of Human Experimentation drawing on the Declaration of Helsinki " (NHMRC, 1966) - Funded research and/or data involving humans must be approved by a Human Research Ethics Committee (1985)     **National Health and Medical Research Council (NHMRC)** First NHMRC established 1937 Provides research funding - upholds ethical standards Develops health and medical guidelines- amongst policies and procedures Upholds ethical standards Informs community via government All HRECs are registered with NHMRC   **Ethics and Integrity in research** Professional, ethical responsibility: \- Conduct research with honesty, integrity throughout process (eg not to falsify) \- Research must be meaningful \- Declare potential conflicts of interest \- Credit authorship to contributors   **National Statement: Main Themes**   **Autonomy, Beneficence, Non-maleficence, Fairness** Autonomy - CONSENT- right to make decisions about their lives Beneficence - BENEFIT - ensure there\'s benefit to what we\'re doing Non-maleficence --RISK- determining what the risk may be   What is Risk? Types of harm discomfort: Severity of harm and consequences Inconvenience -- time, cost   **Researcher responsibility and Risk** Determine level risk Strategies to minimise unavoidable risk Weighing up risk vs. benefits   **Consent** Must be voluntary Must be informed How will the researcher make sure the participants understands purpose, methods, demands, risks and potential benefits? Process and notification of ability to decline to consent and withdraw consent   **What is Risk?** Vulnerable Groups Can you think of any vulnerable groups in society? Can everyone give consent? Can everyone understand consent? Accessibility of information --Reading age --Communication impairment --Cognitive impairment --Culturally and Linguistically diverse People in dependent or unequal relationships   - Risk, benefit, consent= guide decision making - Ensuring culturally and linguistically responsive **ETHICAL PRINCIPLES IN ABORIGINAL & TORESS STRAIT ISLANDER RESEARCH**      **The Lowitja Institute** Health and wellbeing of Aboriginal and Torres Strait Islander people Quality Research Knowledge exchange and translation Supporting Aboriginal and Torres Strait Islander researchers and the health care workforce "Lowitja Learning" = courses (including Indigenous Knowledge Translation) Led by Aboriginal and Torres Strait Islander representatives, community controlled (2020) Engages with not-for profit organisations, government, philanthropists, peak bodies and other professional organisation.   **Ethics: Aboriginal and Torres Strait Islander research**   National Statement on Ethical Conduct in Human Research 2023 (NHMRC, 2023)   All human research in Australia must be approved and monitored by registered HREC (Human Research Ethics Committee)   "... non-Aboriginal HRECs should [consider] referring research proposals to an Aboriginal HREC for approval, create an Aboriginal and Torres Strait Islander subcommittee or reference group, or expand committee membership to include Aboriginal and Torres Strait Islander members and community." p.19  \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ **Ethics in Aboriginal and Torres Strait Islander health research Discussion paper (Lowitja Institute, 2024)** Of around 200 HRECs in Australia, only 3 are Aboriginal HRECs: 1. The Aboriginal Health & Medical Research Council Ethics Committee (AH&MRC, NSW) 2. The Aboriginal Health Research Ethics Committee (SA) 3. The Western Australian Aboriginal Health Ethics Committee (WAAHEC) Victoria, QLD and Tasmania requirement for Aboriginal approval depends on individual HREC Recommendation \#2: Aboriginal HRECs in all states and territories   **AH&MRC Ethical Guidelines: Key Principles (2020)** 1\. Net Benefits for Aboriginal people and communities 2\. Aboriginal control of research 3\. Cultural sensitivity 4\. Reimbursement of costs 5\. Enhancing Aboriginal skills and knowledge     **What you need to know for now....\#1** Understand the AH&MRC (2020) five key principles (AH&MRC, 2023 pp.4-5). You can also read about these in the Lowitja Institute Discussion paper (2024, p.21).      **What you need to know for now....\#2** 1\. Read an article! Apply your knowledge to a real piece of research. We will revisit this article later in the semester   "Sharing and valuing older Aboriginal people's voices about social and emotional wellbeing services: a strength-based approach for service providers" Gibson et al. (2020)   2\. In particular, focus on the Methods and Results sections (pp. 482-485) so you can answer questions about the following:   a\. How do the methods used reflect the 5 key principles of the AH&MRC (2020;2023)?   b\. How was the research project approved? Why is this important?   c\. What were the three themes? How do the subthemes reflect the main themes? Test yourself in the quiz below.            - Bibliographic databases- CINAHL or PubMed Peer reviewed literature - Grey literature- government reports, Aus Beuro of statistics... not research literature can inform us on what we should be doing and researching - Unpublished: non-peer reviewed or gone through rigorous looking over          **Week 3** Lecture 1: Introduction to Foundational Statistics   **Measurement** As allied health professionals we use measurement to understand, and evaluate different characteristics / behaviours of people Measurement = assigning numerals to variables We can ***rarely*** measure directly. I can measure someone's height, but I can't measure directly someone's quality of life We use ***constructs*** to represent how someone with a characteristic /behaviour/trait should react Constructs are "\...associated with some value or values that are assumed to represent the original variable"   **Assessment** An assessment will either be - Discriminative (discriminates between people with and without condition) (e.g. Diagnosis tools, screening tools) - Evaluation (change over time) (e.g. Measuring outcomes) - Predictive (predictive of ability in the future) **Validity** Validity\> Does the assessment measure what it intends to??   - Make inferences from test scores - A ruler measures length using centimetres. - Leg length does not measure back pain   **Reliability** - Consistency of scores and free of error - Must have confidence in the data we collect - Otherwise we cannot draw any conclusions - Always some error in our measurements (we are only human!) - Systematic error: predictable, consistent, over or under estimate, easily amended. - Random errors: Due to chance (e.g. Mechanical inaccuracy or simple mistakes). - Sources of errors - Individual: tester or rater - Measuring instrument - Variability in the characteristic.   **Test-Retest Reliability** - Obtain same results with repeated administrations - Consider - Time between administrations (so no change occurs) - Consistency of test environment - Carryover and testing effects (e.g. Remembering answers) - Where responses are labile test-retest may be impossible to test - What Statistics? - [Intraclass Correlation Coefficient (ICC)] - [Kappa Statistic and % agreement] - [Standard Error of Measurement] - [Pearson or Spearmans correlation (BUT ICC IS PREFERRED)] **Rater Reliability** - Intrarater Reliability - Stability of results by one rater across 2 or more trials - Consider rater bias (may be influenced by their memory of scores, not always possible to eliminate, e.g. Manual muscle test). - Interrater Reliability - Variation between 2 or more raters measuring the same subject - What statistics? - Intraclass Correlation Coefficient (ICC). - Make sure they measure the same way to reduce errors   **Internal Consistency** - Internal Consistency - Extent to which the items of the assessment measure various aspects of the same characteristic - For example, a test on ADL ability should be related to aspects of ADL ability only. IADL ability should not be included. - Statistics? - [Cronbachs alpha (α)] - [Item to total correlations with pearsons product-moment correlation coefficient] - [Spearman-Brown prophecy statistic (for split half reliability only). ]   **Content validity** - Content validity - Does the instrument represent what it claims to measure? - A test of gross motor should not include a language assessment - Determination is subjective - Use a panel of 'experts' - Agreement of content domain = content validation   **Construct validity** - Construct - Ability to measure the abstract concept - Meaning of construct -- based on assumption of how a person with that trait would behave - Make assumptions - Construct validity is supported when theoretical assumptions are supported - Methods - [Convergent/divergent validity (pearson correlations (r) spearmans correlations (r~s~)] - [Factor analysis (reviewing underlying concepts and dimensions)] - [Hypothesis testing (generally establishing a correlation so look for r and r~s~)]     **Criterion validity** - Criterion Validity - Assessment has been assessed against a gold standard - Concurrent (consider comparisons against gold standard) - Again look for correlations or ROC curves - Predictive (assessment predicts the gold standard) - Correlations or ROC curves - Gold standards - Needs to be agreed upon gold standard - Lack of agreement re: gold standard for functional ability - See how much the ROC curves and correlations to help us to determine how much a assessments agrees with the gold standard  **Responsiveness** - Measuring clinically important change over time - Especially important for ***outcome measures*** - Statistics often used - [Effect sizes] - [Standardised response means] - [Guyatt's responsiveness index] - [Standard Error of Measurement] - [Receiver Operating Characteristic Curves] - [Comparing change to a global measure of change] - Tells us how much a person is changing - And how much has happened   **Where is the evidence?** There are lots of systematic reviews on measurement properties (or sometimes called psychometric properties). Save yourself time and look at completed systematic reviews.     **Rules of measurement** Must have a criteria for assigning values What the attribute means    Nominal/ categorical measurement are the same Lecture 2: Descriptive Statics   **Using Data** - Data that you collect can be used to - describe (descriptive statistics) or - to make inferences about a population from your data (inferential statistics) - But using descriptive or inferential depends on your type of data and your research question!   **Population & Samples** - A population = entire group you want to draw conclusions about [*μ*]{.math.inline} - **Sample** = specific group you will get information from[\$\\overline{x}\$]{.math.inline} - Samples -- we cannot realistically measure the entire population, so we take a subset of the population, i.e. a sample. - There is always some sampling error - Not everyone in the population has equal opportunity to participate - Laws of chance - Sample method is not random - Sample is too small to accurately represent the population **Descriptive Statistics** Central tendency (median / mode / mean) 1 3 4 6 6 7 8 A- positive skew C- negative skew       **Spread & variance** Spread of data (range, minimum and maximums, quartiles / interquartile range, % and standard deviations) 1 3 4 6 6 7 8 Range: 7 Min: **1** Max: **8**, Quartile (25%) = **3**. IQR= Q3-Q1, =**4**   **Closer look at standard deviation (SD)** - Approx amount that the scores differ from the mean - To work out the standard deviation you need to know the variance - Variance tells us how spread-out scores are from the mean\ SD helps us see the how dispersed our data is - Z-score is the number of SD a raw score is above and below the mean   **Clinical value** - The mean is a *point* estimate - Confidence interval: lower and upper limit which the true mean should fall - Helps gives us confidence that the mean is accurate - Generally speaking we apply a 95% CI - 0.95 of a curve is.475 either side of the mean - For a z-score this is 1.96 \ [\$\$CI = \\overline{X} \\pm \\left( z \\right)s\_{\\overline{X}}\$\$]{.math.display}\  \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_  **Lecture 3: Inferential Statistics**   **Probability** Important concept for understanding inferential statistics Probability is the likelihood that any one event will occur given the possible outcomes Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events. Probability theory helps us determine if the sample distribution does or does not apply to a population (i.e. is it chance/error or likely true)....This leads us to hypothesis testing **Hypothesis testing** A null hypothesis: A statement of no difference or no relationship between the variables Alternate hypothesis: Negation of the null hypothesis. - Is x treatment more effective than another? - Is there a relationship between patient demographics and improvements observed? - Requires comparison between means, proportions and correlations - *This is an example of statistical inference, i.e. inferential statistics.* Help us compare means, perorations, correlations against probability **P-value** *p*-value is about determining the probability (assuming the null hypothesis was true). If the *p-*value is **high,** \>0.05 then its likely that the null hypothesis is true If the *p*-value is **low,** \