Emer 107 LO8: Research and Quality — Paramedicine Notes (Comprehensive)

Research is classified as quantitative or qualitative.
The goal is to observe, measure, and infer truths about interventions and outcomes.
Proving causality is hard; study design impacts confidence in causal claims.
Methodology covers broad approaches, data collection, and analysis.
Research should be evidence-based, not based on tradition or single opinions.
In paramedicine, systematic research is crucial; experience alone is insufficient.
The Tri-Council Policy Statement provides ethical guidance.
Historical examples (e.g., thalidomide) show once-accepted treatments can be harmful.
Evidence-based practice improves patient outcomes.

Quantitative research focuses on description, measurement, and causality using numerical data and statistics from large groups. Aims to generalize findings.
Qualitative research focuses on understanding meanings, experiences, and processes from participants' perspectives, using rich descriptions from smaller samples. Explores why/how.
Mixed-methods combine both approaches for a broader view.

Paramedic practice must be guided by evidence, not solely tradition or authority.
Science, research, and evidence inform practice and system design.
Research extends knowledge through disciplined, systematic investigation beyond immediate situations.
It helps design systems, guide operations, and inform clinical practice, preventing harm from insufficient evidence (e.g., MAST trousers).
Qualitative insights provide context to quantitative data.

Research: An undertaking to extend knowledge via systematic investigation, aiming to advance knowledge beyond a single situation.
It informs paramedicine practice, policy, and understanding.

Experimental studies (interventional): Researchers intervene, assigning treatments (e.g., prehospital thrombolytics). Often include intervention and control groups.
- Experimental design: Random assignment (randomization) to groups, common in Randomized Controlled Trials (RCTs) ", the "gold standard" for causal inference.
- Quasi-experimental design: Group assignment without true randomization, used when RCTs are not feasible.
Observational studies (non-interventional): Researchers observe exposures and outcomes without assigning treatments.
Inference: Generalizing findings from a sample to a wider population.
Randomization helps control for confounding factors.
External validity: Generalizability of findings to other populations/settings.
Internal validity: Confidence that conclusions reflect true relationships, not biases.
There are trade-offs between internal and external validity.
Causality: The idea that an intervention directly causes an outcome. RCTs are best for causal claims.

Internal validity: Are conclusions accurate for the study design (e.g., free from bias or confounding)?
External validity: Can results be generalized beyond the study sample?
Both are vital; study designs balance them.
Demonstrating causality in real-world settings is complex due to many uncontrolled factors.

Bias: Systematic deviation from truth due to study design/execution.
Confounding: An extraneous factor influencing both exposure and outcome, creating a false association.
- Can be controlled by design (randomization) or statistical analysis.
Selection bias: Systematic differences between study participants and non-participants.
Misclassification bias: Incorrect categorization of exposure or outcome.
Random error: Fluctuations by chance when estimating effects.
- Type I error (false positive): Concluding a difference exists when it does not $\alpha$ .
- Type II error (false negative): Failing to detect a real difference $\beta$ .
- Study power and sample size planning mitigate these errors.

Cross-sectional studies: Data collected at one point in time; cannot infer causality (e.g., survey on burnout).
Case-control studies: Compare people with an outcome (cases) to those without (controls), looking back at exposures (e.g., burnout cases vs. controls for stress exposure).
Cohort studies: Follow groups based on exposure over time to see who develops the outcome (e.g., following high-call-volume paramedics for burnout).
Choice of design depends on the question, existing knowledge, ethics, time, and resources.

Qualitative research aims to understand experiences, meanings, and social processes.
- Descriptive qualitative: Summarizes phenomena in pragmatic, everyday terms.
- Phenomenology: Analyzes lived experiences and how individuals make sense of them.
- Grounded theory: Generates a conceptual framework or theory from participants' views. Typologies: positivist and constructivist.
- Ethnography: Understands culture within a group, often via participant observation (e.g., paramedics' interprofessional collaboration).
Reflexivity: Researchers acknowledge and examine how their perspectives influence the study.
Mixed-methods: Combine qualitative and quantitative approaches.
Rigor in qualitative research emphasizes credibility, transferability, dependability, and confirmability, though criteria can be context-dependent.

Consider research question, feasibility, ethics, and required causality level when choosing a design.
RCTs offer strong causal inference but may be impractical; other designs have trade-offs.
Understand and address bias, confounding, and random error.
Qualitative methods provide depth and context that quantitative data cannot.
Qualitative and quantitative methods are complementary.
In paramedicine, evidence should guide practice and system design, with ongoing evaluations
Cross-sectional study
Case-control study
Cohort study
Descriptive qualitative study
Phenomenology
Grounded theory (positivist vs constructivist)
Ethnography
Reflexivity
Mixed-methods study
Tri-Council Policy Statement (as a guiding ethical framework)