IPE 315: IEBPHP Final Exam

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.