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Purpose of Research in Healthcare:
Improve patient outcomes.
Use evidence-based practices to enhance healthcare quality.
Evidence-Based Practice (EBP):
Combines the best research evidence, clinical expertise, and patient values.
Promotes decision-making based on reliable research.
Quantitative Research:
Focus: Numerical data, hypothesis testing.
Tools: Surveys, experiments, statistical analysis.
Examples: Randomized controlled trials (RCTs), regression analysis.
Qualitative Research:1
Focus: Human experiences, meanings, and social contexts.
Tools: Interviews, focus groups, participant observations.
Methods: Phenomenology (lived experiences), Ethnography (cultural dynamics), Grounded Theory (theory generation).
Mixed-Methods Research:
Combines qualitative and quantitative approaches for comprehensive insights.
Balances depth (qualitative) with breadth (quantitative).
Experimental Design:
Tests cause-and-effect relationships using control and intervention groups.
Features: Randomization, blinding, controlled variables.
Correlational Design:
Examines relationships between variables without establishing causation.
Uses tools like Pearson's correlation coefficient.
Phenomenology:
Explores lived experiences to identify common themes.
Ethnography:
Studies cultural behaviors, norms, and interactions within a group.
Nominal Data:
Categories without order (e.g., gender, eye color).
Ordinal Data:
Ranked data with unequal intervals (e.g., satisfaction scales).
Interval Data:
Equal intervals without a true zero (e.g., temperature in Celsius).
Ratio Data:
Equal intervals with a true zero (e.g., weight, height).
Quantitative Tools:
Questionnaires and structured surveys.
Statistical analysis: t-tests, Chi-square tests, ANOVA, regression.
Qualitative Tools:
Open-ended interviews.
Observations in natural settings.
Coding for thematic analysis.
Triangulation:
Uses multiple data sources/methods to validate findings.
Descriptive Statistics:
Summarize data using measures like mean, median, and standard deviation.
Inferential Statistics:
Draw conclusions from sample data to generalize about a population.
Key measure: p-value (<0.05 indicates statistical significance).
Informed Consent:
Ensures participants understand study risks and benefits.
Beneficence:
Researchers must minimize harm and maximize benefits.
Confidentiality:
Protects participants' personal data.
Dependability:
Findings are consistent and repeatable.
Confirmability:
Data reflects participants' input, not researcher bias.
Transferability:
Applicability of findings to other contexts.
Reflexivity:
Researchers reflect on and minimize their influence on the study.
CFIR (Consolidated Framework for Implementation Research):
Examines factors influencing research implementation.
PARIHS (Promoting Action on Research Implementation in Health Services):
Focuses on evidence, context, and facilitation.
RE-AIM Framework:
Evaluates research implementation across Reach, Effectiveness, Adoption, Implementation, and Maintenance.
Systematic Reviews:
Summarize existing research to identify trends and gaps.
Meta-Analyses:
Combine statistical results from multiple studies for broader insights.
Audit Trails:
Document research processes to ensure transparency.
