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94 Cards in this Set
- Front
- Back
- 3rd side (hint)
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5 step EBNursing Process |
P- problem identification E- evidence review A- appraise evidence C- care integration E- evaluate care integration |
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Types of Studies |
1) Experimental Studies 2) Observational Studies 3) Qualitative Studies 4) Quantitative Studies |
EOQQ |
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Experimental |
Use of intervention or treatment A) RCT - randomized controlled trial B) Quasi- Experimental - not randomized |
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Observational Studies |
A) collect data without intervening -cohort -case control -cross sectional |
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Observational - COHORT |
Population that is exposed and you want to see outcome (present to future) prospective |
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Observational- CASE CONTROL |
Population exposed and research contributing factors Past to present (retrospective) |
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Observational - CROSS SECTIONAL |
Snapshot in time Present (one point in time) |
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Quantitative Research Concepts |
Explain/predict, numerical/statistical findings 1) Study purpose/aim 2) study population/sampling 3) variables 4) measurement 5) error 6) hypothesis testing |
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Quantitative- STUDY PURPOSE |
Objective of the study |
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QUANT- Research Hypothesis |
Predicted answer to research question, **never proved or disproved |
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QUANT - Research Hypothesis |
1) Directional 2) Non-directional 3) Null |
DNN |
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QUANT - RESEARCH- directional |
Predicts the direction |
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QUANT- RESEARCH- Non directional |
Predicts the existence but does not define direction |
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QUANT- RESEARCH - Null |
Expresses the absence of relationship Used on in statistical testing to show how confident the results were not from chance alone Status quo- H0= $13k H1 not equal to $13k - change / intervention |
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Why are Observational Studies inferior to RCTs in the evidence hierarchy? |
Designs by their nature are more subject to bias than RCT |
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Confounders (extraneous variables) |
When not adjusted for in study design or data analysis, distort study results |
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Point estimate |
Information from a sample is used to estimate the single value that best represents the population parameter |
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Confidence Interval |
Provides a range about the point estimate 95% CI means that we are 95% confident that the true population parameter lies within range The wider the CI - the less precise the results (so the less confidence we have in them) |
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P value |
Probability that study findings are due to random error i.e. chance |
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You are appraising the internal validity of a research article. As such, you are most interested in: |
Accuracy of study findings |
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You are appraising the reliability of a student instrument. As such, you are interested in whether: |
The instrument yields results consistently |
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QUANT-Population/Sampling |
1) population 2) sample 3) inference |
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QUANT-POPULATION |
Total group of people that study aims to investigate |
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QUANT- SAMPLE |
Smaller representation of population (hopefully) that is studied |
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QUANT-POPULATION-Inference |
Responsible judgment that they believe study results can be referred to the greater population |
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QUANT- Variables |
Attributes of patients/clinical events that vary/can be measured 1) Independent 2) Dependent |
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QUANT- VARIABLES- Independent |
The cause or predictor; the intervention |
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QUANT-VARIABLES-Dependent |
The outcome/result of the intervention |
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QUANT- Measurements |
Assignment of numbers to represent amount of attribute |
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QUANT-MEASUREMENT-Validity |
Accuracy 1) Face validity 2) Content validity 3) construct validity |
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QUANT-MEASUREMENT-Validity |
Accuracy 1) Face validity 2) Content validity 3) construct validity |
FCC |
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Face Validity |
Does tool measure what it is supposed to measure Superficial (weak measurement) , least scientific |
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Content validity |
Extent to which an instrument includes dimensions of phenomenon (measuring pain includes questions about severity and sensation of pain) Often checked by a panel of experts |
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Construct Validity |
Does the instrument perform as expected between similar and dissimilar constructs (something that is hard to observe) —- for ex. Integrity 1) convergent - similarities b/w pain/distress scores 2) discriminant -pain scores differ from intelligent scores |
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Instrument reliability |
Consistency with which an instrument measured the target 1) internal consistency- all items are measuring the same attribute (at one time) — evaluated by administering instrument at one time —-appropriate for multi-item instruments —-most widely used approach to assessing reliability —-assessed by computing coefficient alpha |
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Stability |
Extent to which scores are similar on two separate admin of instrument |
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4 levels of measurement |
Assignment of numbers to represent the amount of attribute present in an object or person- using specific rules |
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4 levels of measurement |
Assignment of numbers to represent the amount of attribute present in an object or person- using specific rules 1) nominal 2)ordinal 3) interval 4) ratio |
NOIR |
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MEASUREMENT- Nominal |
Categories, but no order —-Gender , Ethnicity |
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MEASUREMENT- Ordinal |
Ranking objects , but size of interval not specified ——how strongly do you disagree or agree on a spectrum survey ——grading system at Columbia |
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MEASUREMENT- Interval |
On a scale with equal distance but no ZERO; order matters, no true zero at starting point ——température |
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MEASUREMENT- Ratio |
On a scale with equal distance, but has a zero at starting point , order matters —-height , age, height |
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P value = 0.02 |
It means that there’s roughly a 2% probability that study results are due to chance |
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Relative Risk (95% CI) = 0.55 (0.34-0.90). Does CI show statistical significance ? |
Yes, 0.34, 0.90 does not cross 1.0 CI interprétation: We are at 95% confident that the true relative risk is between these bounds |
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Clinical practice guideline (CPG) |
statements that include recommendations intended to optimize patient care and are informed by a sytematic review of evidence. NOT a one size fits all to patient care. offers an evaluation of the quality of the relevant scientific literature and an assessment of the likely benefits and harms of a particular treatment |
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Strengths of CPG |
comprehensive addresses multiple research questions offers variety of info for the mgmt of a whole health problem |
original studies examine only 1 aspect of mgmt (are individuals with AMS at risk for falls) provides summary of sev aspects of a health condition a mega systematic review (diff questions complied) |
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Limitations of CPG |
Subjective judgments - leaves room for error and bias (who is on the panel?) Conflicts of interest - biggest limitation- financial and intellectual poor methodology - not follow recommended guidelines limited panel composition |
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Considerations of CPG |
generalizability- not reflect your pt population funding is expensive lack of transparency in recommendations (rating strength of evidence) funding and resource limitations (who is on the panel) updating clinical guidelines - updated as new evidences comes out |
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AGREE |
The Appraisal of Guidelines for research and evaluation I & II instruments used for CPG appraisal, internationally - GOLD STANDARD methodological rigor and transparency |
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AGREE Domains |
1) Scope & Purpose 2) Stakeholder Involvement 3) Rigor and Development 4)Clarity of Presentation 5) Applicability 6) Editorial Independence |
1)f do we know the research question that guideline is addressing 2) Who are experts? specific to 1 discipline? limits? 3) did not use rigorous methods- be skeptical 4) easy to read? 5) who funded the study |
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CPG quality assessment |
"CPG offer an evaluation of the quality of the relevant scientific literature." GRADE approach Wavy lines |
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CPG quality- GRADE approach |
rates quality of evidence and begins with study design address 5 reasons to possibly rate down the quality of evidence and 3 to possibly rate up the quality major amount based on judgment - need to do qualitative assessment |
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CPG quality - Wavy lines |
pyramid levels of evidence while RCT designed to minimize confounders and bias-- does not mean it's well conducted - might be conducted poorly (take this into account) Some case control can provide same level of evidence as RCTs if done well |
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How does evidence translate into what I'm recommending? |
HIPAC - strong, weak, or no recommendations recommending things based on net benefits, does not have to be an RCT study |
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Quantitative |
Goal is to explain, predict, control outcomes (#s, deductive, generalizable, objective) |
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Quantitative Methodology |
The rationale for - and the lens through which analysis occurs scientific method - problem, hypothesis, experiment, data analysis, conclusion, repeat |
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Quantitative methods |
RCT systematic review quasi experiments |
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Qualitative |
Goal: interpret experiences and meanings credibly (subjective, inductive, words, not generalizable) produce credible representations of pts' opinions, needs, values or experiences |
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Qualitative |
will not test hypotheses, findings to generate theories to be tested quantitatively |
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Qualitative |
A systematic and rigorous inductive approach to capture patients' experiences, beliefs, customs, etc. |
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Qualitative Methodology |
more of a circle!!! the rationale for and the lens through w/c analysis occurs Multiple Truths - represent experience for that population; might go back and ask questions to same/different population |
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Qualitative descriptive/methods |
used when little is known about a phenomenon not highly interpretive in depth interviews with 20-30 individuals line by line latent coding using conventional content analysis looks like frequency list or report of common values or preferences |
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Qualitative GROUNDED THEORY |
focuses on the discovery of a basic social problem that a defined group experiences to produce a theory in depth interviews with 20-50 people iterative data collection, data analysis, and sampling constant comparison to develop and refine theoretically relevant categories |
iterative means repetition |
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Qualitative Phenomenology |
Philosophical- lived experience focuses on the description and interpretation of people's lived experience (meaning) researchers suspend their beliefs (bracketing) 4 keys aspects of experience: lived space, lived time, lived human relation (disease, losing parent to violent crime) in depth multiple conversations with 5-25 participatns (smaller group) who have experienced the phenomenon |
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Qualitative Ethnography |
through anthropology describes and interprets a culture (first semester masters students at Columbia) [anyone who has shared experience]; assumes that culture structures experience relies on extensive, labor-intensive fieldwork active participation - non active observation (want to observe as many ppl as possible!!) seeks an emic perspective (insider's view) everyone in the culture is studied (30-60) |
emic-- part of the culture |
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Scientific methods |
to reduce bias, randomly select cases/subjects, randomize to conditions and stop enrollment based on power calculation |
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Bias reduction for qualitative |
purpose sampling - dismiss random selection, want people we can actually study {sounds bias to me actually} |
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Qualitative - Participants |
participants are not called subjects; power is shared!! not subjects but they are participants and subjects. sampling is purposeful samples are small in size depth and breadth - shared experience, shared culture enrollment ends w/ data saturation |
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Qualitative- scientific method |
to reduce bias, conduct high fidelity standardized research that is reproducible |
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Qualitative- instrument |
should not use multiple interviewers - need >1, audit/train together, asking the same questions skill of the researcher is essential- be understanding and supportive but NOT therapeutic [RAPPORT] prolonged engagement - stay w/ them for a long time (1-2hrs and multiple interviews) acknowledgment of bias interviews are iterative |
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Qualitative -Scientific method |
to reduce bias, collect objective data or use valid and reliable tools |
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Quantitative Data Collection |
often occurs longitudinally- done multiple times! biological lab measures outcome data survey responses |
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Qualitative Data Collection |
occurs ONCE - all subjective! look at it 1 time! |
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Qualitative - Triangulation |
triangulation - observing and interviewing - BEST way to collect data Interviews - stronger! #s are better focus groups observation analysis of documents analysis of art based creations |
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Qualitative- Scientific methods |
to reduce bias, uses statistical tests, determined before study starts |
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Qualitative- Code |
multiple independent coders all trained to code the same way or to ff the same codebook search for patterns/variations in the data and compare them validate findings; resolve conflicts through consensus building we call them findings, not results share findings back with participants to determine if we have captured their experiences (do this to improve rigor of our designs! confirm/reduce researcher bias that may have been introduced |
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Qualitative Coding |
start broad/focus data refine themes theoretical concepts coding and observation exercise - two separate notes to see if we're looking too far into own prejudices |
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Qualitative Coding |
patterns of data note - what are they saying? how can it be organized? |
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Qualitative Fieldnotes |
observations = nonverbal responses where are they, what are they wearing? what does this tell you? what are they not saying- body languange, tone of voice... |
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Qualitative - assessing rigor!! STEP 1 METHODOLOGY |
checklist of indicators of qualitative studies! Step 1: do they state methodology? are the methods consistent with methodology? have the authors identified and are the methods? research question identification- w/ providing hypotheses |
credibility - telephone interviews, lasting between 25-71 mins, not great. want multiple. |
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Qualitative - Assessing Rigor STEP 2 RIGOR - CREDIBILITY |
detailed methods plausibility of data analysis and interpretation; enhanced w/ prolonged engagement w/ participants and keeping detailed field notes (representation of truth!) |
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Qualitative - Assessing Rigor STEP 2 DEPENDABILITY |
the stability of a data analysis protocol should be well described- who did it, how they did it and how faithful the methodology and protocol who coders are how divers they are if they all code the same way! |
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Qualitative - Assessing Rigor STEP 2 CONFIRMABILITY |
verify a specific phenomenon of interest using different sources of data before generating conclusions data included 26 transcribed audio recorded interviews from 25 families, parent-reported demographic information, and health information about the fetal diagnosis drawn from maternal medical records, field notes were kept |
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Qualitative - Assessing Rigor STEP 2 TRANSFERABILITY |
explanations of how the sample was selected and descriptive characteristics of study participants which provides a context for the results and enables readers to decide if other samples share critical attributes detailed description of participants |
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Qualitative - Assessing Rigor STEP 2 TRANSPARENCY |
Clearly articulate study procedures and data analysis strategies clear rationale for selecting a particular population or people with certain characteristics identifies the research question being investigated and why this research question was selected (the gap in knowledge or understanding that is being investigated) describes recruitment, enrollment, data collection, and data analysis |
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Qualitative - Assessing Rigor STEP 3 STUDY DESIGN & METHODS- Theory |
Theory- describe how theory informed the study, including research questions, design, analysis, and/or interpretation use methodological congruence as a guiding principle if divergence from theory occurs, explain and justify how and why theory was modified |
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Qualitative - Assessing Rigor STEP 3 STUDY DESIGN & METHODS- Sampling & sample size |
following the concept of transferability, clearly describe sample selection methods and sample descriptive characteristics, and provide evidence of data saturation and depth categories describe any changes to data collection methods made over the course of the study (ex. modifications to interview guide) |
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Qualitative - Assessing Rigor STEP 4 Data Analysis |
Implement, document, and describe a systematic analytic process (use of code book, development of codes, prior codes, emergent codes, how codes were collapsed, methods used for coding, memos, coding process. |
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Qualitative - Assessing Rigor STEP 4 Data Analysis- coding reliability |
provide info on who comprised the coding team (if >1 coders were used), and coding training and process, with emphasis on systematic methods, including strategies for resolving differences between coders |
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Qualitative - Assessing Rigor STEP 4 Data Analysis- Method of organizing data |
computer software or manually describe how data were organized. If qualitative computer software was used, provide name and version number of software used |
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Qualitative - Assessing Rigor STEP 5 Presentation of Findings |
Should have exemplar- quotations by subjects |
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Qualitative - Assessing Rigor STEP 5 Presentation of Findings- Results & discussions |
provide summaries and interpretations of the data (themes, conceptual models) and select illustrative quotes present the findings in the context of the relevant literature |
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Qualitative - Assessing Rigor STEP 5 Presentation of Findings- Quantification of results |
consider whether quantification of findings is appropriate. If quantification is used, provide justification for its use. |
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Qualitative - Assessing Rigor STEP 5 Presentation of Findings- Findings (not results) |
used for coding to define each phase used exemplars direct quotes used to illustrate the richness of the data quantify when needed |
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