Part 1 5/21/2025 Research and Evidence-Based Practice Lecture

Study Session & Birthdays

  • NSNA is hosting activities (voluntary).
  • Francisco posted a flyer for an OB study session to review the study guide. It's optional and open to members and non-members for the first session.
  • Six birthdays this week: Alexis, Jacqueline, Alicia, Kendra, Vicini, and Jocelyn (01/23).
  • Pain assessment tools from two semesters ago will be distributed to show comments.

Introduction to Research and Evidence-Based Practice

  • Professor Magana is absent due to his first wedding anniversary.
  • The class is a quick introduction to research and evidence-based practice.
  • Quizzes are expected to be open book and online.
  • The lecture will cover quantitative, qualitative, and mixed methods research, with a focus on quantitative methods.
  • The aim is to look at the similarities and differences between quantitative, qualitative, and mixed methods.

Levels of Evidence

  • Levels of evidence (1-5) will be discussed; not tested in this class but relevant for future master's courses.
  • Level 1: highest level, experimental study that's randomized and controlled.
    • Explanatory mixed methods design including only a level one quantitative study
    • Systematic review of randomized controlled trials with or without a meta-analysis.
  • Level 2: quasi-experimental study.
    • Explanatory mixed methods design including only a level two quantitative study.
    • Systematic review of a combination of random controlled trials and quasi-experimental studies, or quasi-experimental studies only with or without a meta analysis.
  • Level 3: non-experimental study.
  • Level 4: opinion of authorities or nationally recognized expert committees.
  • Level 5: based on experimental and non-research evidence.

Quantitative Designs

  • Focus primarily on levels 1-3: true experimental, quasi-experimental, and non-experimental designs.
  • Experimental vs. Non-experimental Designs:
    • Experimental: involves manipulation of the independent variable, control, and randomization.
    • Non-experimental (observational): looks at relationships among variables without manipulating independent variables.
    • Reference: Pollitt and Beck research textbook (purple one).

Research Design & Causality

  • Research design involves testing hypotheses about causal relations.
  • Causal means causing a relationship.
  • True experimental designs are best for showcasing causal relationships.
  • Issue with quantitative designs: data can be manipulated.
  • Causality Criteria:
    • Time (temporal): cause must precede the effect.
    • Relationship: empirical (numbers-driven) relationship between cause and effect.
    • No confounders: relationship not explained by a third variable.

Randomized Controlled Trials (RCTs)

  • Three key aspects of RCTs: manipulation, control, and randomization.
  • Manipulation: researcher manipulates one variable.
    • Example: Vetmed study offering dogs a vegetable-based diet trial.
    • Experimenter administers treatment/intervention to some subjects (not others).
    • Intervention protocols must be standardized.
  • Control Group: gets placebo or sugar pill.
    • Participants consent to potentially receiving a placebo.
    • Control group conditions: no treatment, placebo, alternative treatment (e.g., aspirin instead of opioid), standard treatment.
    • Placebo = pseudo intervention.
    • Dosage variations can also be used (e.g., 20mg vs. 40mg vs. 60mg).
    • Delayed treatment: waitlist group.
  • Randomization: patients are placed randomly into groups.
    • Simple randomization: coin flip.
    • Complete randomization: ensures a fully randomized approach.
    • Tools: tables, computers, Canvas.
    • Allocation concealment, baseline data are components.
    • Patient consent required, acknowledging randomized group assignment.
    • Partial randomization can simplify the process.

Blinding

  • Blinding: concealing information, sometimes even from researchers.
  • Double-blind study: involves external parties for randomization and assignment.
  • Bias: avoid expectation, performance, detection/ascertainment biases.

Experimental Designs

  • Common designs: post-test studies, pre-test/post-test designs.
    • Pre-test/post-test: test given before and after intervention (lecture, reading).
    • Expectation: test scores improve after the intervention.
  • Factorial and crossover designs also exist.

Study Strengths & Limitations

  • Master's courses involve creating posters on quality improvement projects, including strengths and limitations.
  • Limitations: can include small group size.
  • Qualitative studies are typically not generalizable.
  • Quantitative studies need n≥50n \geq 50 for generalizability.
  • Strengths: experimental designs are the “gold standard” for intervention effectiveness.
  • Limitations: constraints in clinical settings (e.g., limited nurse participation), artificiality (Hawthorne effect).
  • Hawthorne effect: participants alter behavior to please researchers.