NCM-N 111 Lesson 2: Trends, Gains, Constraints, and Ethics in Nursing Research

Trends and Drivers in Nursing Research

• Definition and Evolution of Nursing Research

  • Nursing research advances continuously in direct response to dynamic changes in the healthcare environment.

  • It generates scientific knowledge that guides nurses in providing safe, effective, and evidence-based care.

  • Modern nursing research has expanded beyond traditional hospital settings into homes, schools, communities, disaster areas, and virtual healthcare environments.

  • Research investigations focus on how nursing interventions improve patient outcomes, reduce overall healthcare costs, enhance patient satisfaction, and strengthen health systems.

• Drivers of Healthcare Evolution

  • Healthcare is continuously transformed by advances in science, technology, population growth, globalization, environmental changes, and emerging diseases.

  • Changing societal dynamics lead to patients presenting with increasingly complex health conditions, chronic illnesses, mental health concerns, and diverse cultural backgrounds.

  • Environmental context: Global environmental shifts, such as global warming caused by the greenhouse effect, contribute to a temperature increase of $1.1\text{--}1.6 tribunal^\times$ or $0.6\text{--}0.9^\times$ equivalent ($1.1\text{--}1.6 drop^\text{F}$ / $0.6\text{--}0.9 drop^\text{C}$), necessitating ongoing adaptations in healthcare delivery.

  • Recent global health events, notably the COVID-19 pandemic, demonstrated the critical need for healthcare professionals to adapt rapidly and create innovative care delivery models.

• Key Trends in Contemporary Nursing Research

  • Evidence-Based Practice (EBP): Systematically integrating research evidence into clinical decision-making.

  • Artificial Intelligence and Health Informatics: Leveraging digital data systems and intelligent technologies to enhance patient care and operational workflows.

  • Telehealth and Remote Patient Monitoring: Utilizing technology to evaluate and monitor patients remotely.

    • Clinical Example: During the COVID-19 pandemic, telehealth became a vital strategy to monitor patients while minimizing unnecessary hospital visits. Pandemic-era research proved telehealth improved healthcare access and patient satisfaction, leading institutions to permanently adopt virtual consultation services.

  • Precision and Personalized Care: Tailoring interventions to individual patient genetics, environments, and lifestyles.

  • Mental Health and Community Health: Addressing psychological well-being and health outcomes across broader population groups.

  • Disaster Preparedness: Developing protocols and strategies to manage health crises, natural disasters, and global emergencies.

• Core Purpose of Research Trends

  • Allows nurses to improve patient safety.

  • Enhances clinical decision-making capabilities.

  • Facilitates the development of innovative clinical interventions.

  • Promotes high-quality, evidence-based healthcare systems.

Evidence-Based Practice (EBP) and Gains of Research

• Core Components of Evidence-Based Practice

  • EBP requires nurses to make clinical decisions by integrating three distinct elements:

    • Best Available Research Evidence: Empirically sound findings from scientific studies.

    • Clinical Expertise: The nurse's professional judgment, skill, and cumulative practical experience.

    • Patient Values and Preferences: The individual needs, cultural beliefs, and choices of the patient.

• Clinical Applications of EBP

  • Wound Care: A hospital updates its clinical wound care protocol after empirical research demonstrates that an alternative dressing technique accelerates tissue healing.

  • Infection Prevention: Rigorous research demonstrates that proper hand hygiene drastically reduces hospital-acquired infections (HAIs). Consequently, healthcare facilities globally enforce strict hand hygiene protocols to safeguard patients.

• Gains Generated by Nursing Research

  • Generates scientific evidence that elevates overall healthcare delivery.

  • Strengthens four key pillars of the nursing profession:

    • Nursing Practice: Leads to better clinical guidelines and direct improvements in patient outcomes.

    • Nursing Education: Informs curriculum design with contemporary, validated clinical knowledge.

    • Nursing Management: Optimizes resource allocation, organizational systems, and care efficiency.

    • Nursing Policy: Informs institutional, national, and international health policy formulation.

  • Drives continuous professional development and enhances the quality of care.

Constraints and Challenges in Nursing Research

• Common Barriers to Conducting Research

  • Limited Financial Resources: Inadequate funding or budget constraints for carrying out research tasks.

  • Heavy Academic or Clinical Workload: High patient-to-nurse ratios or demanding academic schedules limiting time for inquiry.

  • Insufficient Research Knowledge and Experience: Methodological gaps or lack of formal research training among staff.

  • Difficulty Recruiting Participants: Challenges in obtaining adequate, representative sample sizes.

  • Limited Access to Scientific Databases: Restrictive paywalls or lack of institutional subscriptions to high-impact journals.

  • Time Limitations: Tight deadlines and competing priorities.

  • Ethical Approval Requirements: Navigating complex institutional review processes.

  • Publication Challenges: High rejection rates or prolonged peer-review cycles.

• Impact on Undergraduate Researchers

  • Undergraduate students frequently experience acute time limitations, heavy academic workloads, limited financial budgets, and minimal prior research experience when conducting independent or group studies.

Research Ethics and Regulatory Frameworks

• Purpose of Research Ethics

  • Protects the basic rights, human dignity, and personal welfare of research participants.

  • Ensures scientific findings are valid, credible, and trustworthy.

  • Core Philosophy: Good science must always be ethical.

• Core Ethical Principles

  • Respect for Persons: Autonomous individuals decide freely whether or not to participate in a study without coercion or undue influence.

  • Beneficence: Researchers are obligated to maximize potential benefits while minimizing all possible risks and harms.

  • Justice: Requires fair and equitable selection and treatment of research participants without systemic bias or exploitation.

• International Ethical Guidelines

  • Nuremberg Code: Mandates voluntary informed consent as absolute requirement.

  • Declaratio~ki: Establishes ethical standards for medical research involving human subjects.

  • Belmont Report: Articulates the core principles of Respect for Persons, Beneficence, and Justice.

  • CIOMS (Council for International Organizations of Medical Sciences): Provides detailed international ethical guidance for health-related research involving humans worldwide.

• Philippine Ethical Framework

  • Research Ethics Committee (REC): Responsible for reviewing and approving research protocols involving human participants.

  • Data Privacy Act (RA 10173): Safeguards personal and sensitive information gathered during research.

  • Intellectual Property Code (RA 8293): Protects intellectual property, authorship, and ownership of research outputs.

  • National Ethical Guidelines for Health and Health-Related Research: Sets nationwide benchmarks for ethical compliance in health inquiries.

• Ethical Case Analysis: Academic Coercion Scenario

  • Scenario: A faculty researcher asks students to participate in a study but implies or explicitly states that their academic grades will be adversely affected if they decline.

  • Violated Principle: Violates Respect for Persons by undermining voluntary participation through academic coercion and undue leverage.

  • Correct Protocol: Recruitment must explicitly inform prospective participants that participation is entirely voluntary, declining will carry zero penalty or grade impact, and consent can be withdrawn at any stage without repercussion.

Research Interrelationships, Methodology, and Design Architecture

• Interrelationship of Core Research Elements

  • The research process is an integrated network connecting:

    • Research Problem

    • Variables & Hypotheses

    • Related Literature & Studies

    • Theoretical & Conceptual Frameworks

    • Research Design

    • Data Collection & Measurement

    • Findings, Conclusions, & Recommendations

• Definition of Research Design

  • The researcher’s overall conceptual plan for conducting an investigation.

  • Outlines specific details regarding:

    • Types of data to be gathered.

    • Techniques or means used to obtain data.

    • Instruments used along with their validation procedures.

    • Data analysis scheme, including statistical tools and treatments.

• Sub-Components of Research Methodology

  • Sampling Design: A precise description of the target population, geographic location/setting, and procedure used to select samples or respondents.

  • Instrumentation and Statistics: Explicit description of data gathering tools and the specific statistical treatments applied to analyze data.

• Criteria for Choosing an Effective Research Design

  1. Logically Sound: Aligns internal procedures rationally with the core research question.

  2. Comprehensive: Contains sufficient scope to thoroughly solve the research problem.

  3. Reasonably Clear and Precise: Expresses methodology without ambiguity.

  4. Clearly Spelled Out: Details all steps explicitly so that replication is possible.

  5. Free from Weaknesses: Eliminates methodological flaws or unmanaged confounding variables.

• Main Categories of Research Designs

  • Experimental Designs

  • Non-Experimental Designs

  • Combined Experimental and Non-Experimental Designs

  • Quantitative and Qualitative Designs

  • Mixed Method / Multimethod Designs

Quantitative Research Designs and Classification

• Characteristics of Quantitative Research

  • Objective: Measures phenomena using numerical metrics.

  • Deductive: Tests existing theories and structured hypotheses.

  • Generalizable: Extrapolates findings from a representative sample to a larger population.

  • Data Format: Numerical data subjected to statistical processing.

• Experimental Designs

  • Investigates explicit cause-and-effect relationships under controlled conditions.

  • True Experiments (4 Essential Properties):

    1. Manipulation / Intervention: Active introduction of an experimental treatment by the researcher.

    2. Control: Use of a control group alongside an experimental group to isolate variables.

    3. Randomization: Random selection of subjects from a population and random assignment into experimental or control conditions.

    4. Validity: High internal validity (minimizing confounding influences) and external validity (generalizability).

  • Types of True Experimental Designs:

    • Post-Test Only Design: Data collected only after experimental intervention.

    • Pre-Test Post-Test Design: Baseline measurement taken before treatment, followed by post-treatment measurement.

    • One-Shot Design: Single group evaluated post-intervention.

    • Clinical Trials & Randomized Controlled Trials (RCTs): Gold standard intervention studies using placebos, single-blind (subject unaware), or double-blind (subject and investigator unaware) techniques to control for placebo effects.

    • Solomon Four-Group Design: Combines two pre-tested groups and two non-pre-tested groups to evaluate pre-test sensitization effects.

    • Factorial Design: Evaluates two or more independent variables simultaneously.

    • Randomized Blocked Design: Subjects categorized into homogenous blocks before random assignment.

    • Crossover Design (Switching Replications): Subjects receive multiple sequential treatments, crossing over between control and intervention arms.

  • Quasi-Experimental Designs:

    • Lacks one or more of the 4 essential properties of true experiments (typically lacks randomization or a true control group).

    • Employs a "comparison group" rather than a strictly controlled group.

    • Relies partly on human judgment rather than purely objective assignment criteria.

    • Types: Non-equivalent Control Group Before-and-After Design, Cohort Design, Time-Series Design.

• Non-Experimental Designs

  • Observes phenomena naturally without manipulating independent variables.

  • Basic / Pure / Library Research: Driven by fundamental curiosity to expand scientific knowledge and discover truth without immediate practical application.

  • Applied / Action Research: Performed for practical problem-solving to generate knowledge that directly alters or improves clinical practice.

  • Historical Design: Examines past events to interpret current patterns.

  • Descriptive Designs: Includes Descriptive Normative Surveys, Correlational Studies, Comparative Studies, Descriptive Evaluative Surveys / Methodological Studies, Problem-Solving Studies, Decision-Making Studies, Case Studies, Content Analysis, Feasibility Studies, Cross-Sectional Studies (evaluating data at a single point in time), and Longitudinal Studies (evaluating data over extended periods).

• Combined Experimental and Non-Experimental Designs

  • Known as "partial experiments" or partially controlled non-experimental designs.

  • Executed in natural clinical settings (e.g., a hospital nursing unit) where researchers exercise partial control over group assignments.

• Specific Types of Quantitative Inquiries

  • Surveys

  • Methodological Research

  • Evaluative Research

  • Content Analysis

  • Clinical Trials / Intervention Studies

  • Secondary Analysis

Qualitative Research Designs and Mixed-Method Frameworks

• Characteristics of Qualitative Research

  • Subjective: Captures personal perceptions, lived experiences, and contextual meanings.

  • Inductive: Generates concepts, frameworks, or theories from specific empirical observations.

  • Not Generalizable: Seeks depth of understanding within a specific context rather than broad statistical application.

  • Data Format: Words, text, narratives, visual artifacts, and observational notes.

• Qualitative Research Types

  • Phenomenological Studies: Investigates the essence of lived human experiences.

  • Ethnographic Studies: Examines culture, social patterns, and behaviors of specific groups in natural settings.

  • Grounded Theory Studies: Systematically generates theoretical models directly grounded in empirical data.

  • Historical Studies: Analyzes qualitative primary source documents and narrative histories.

  • Case Studies: In-depth contextual exploration of a single case or bound system.

  • Field Studies: Direct observation and narrative recording in real-world environments.

  • Biographies: Detailed examination of an individual's life trajectory.

  • Critical Theory: Analyzes power dynamics, societal structures, and systemic inequities.

  • Feminist Approach: Focuses on gender dynamics, non-oppressive methodologies, and women's experiences.

  • Participatory Action Research (PAR): Collaborative inquiry involving participants directly in identifying problems and implementing solutions.

  • Focus Group: Interactive group discussions structured to gather shared perceptions.

• Mixed-Method and Multimethod Studies

  • Triangulation: Combines quantitative and qualitative methods to cross-validate findings, providing a comprehensive strategy when rapid responses are required or existing data is sparse.

  • Mixed-Method Integration Strategies:

    1. Applying qualitative thematic analysis to qualitative survey responses.

    2. Incorporating quantitative measurement metrics into ethnographic research.

    3. Embedding qualitative interviews or narrative data collection within experimental designs.

Sampling Concepts, Designs, and Procedural Steps

• Fundamental Terminology

  • Universe: The entire theoretical aggregation of potential subjects or elements.

  • Population:

    • Target Population: The entire aggregation of cases meeting specified criteria about which the researcher wishes to make generalizations.

    • Subjects / Respondents: Individual units from whom data is gathered.

    • Stratum: Mutually exclusive sub-population divisions within a broader population.

    • Eligibility / Inclusion Criteria: Precise characteristics required for inclusion in the sample.

    • Exclusion Criteria: Specific characteristics that disqualify a subject from participation.

  • Sampling Frame: The actual comprehensive list of population elements from which the sample is drawn.

  • Sampling Design: The operational strategy and mathematical formula (e.g., Sloven Formula) used to select sample units.

  • Sample Size: The precise number of subjects selected for inclusion.

  • Power Sampling: In qualitative and experimental research, power defines sample size adequacy. Adequate power ensures sufficient sample size to detect statistical significance or detect true differences in dependent variables.

• Five Steps in the Sampling Process

  1. Specify inclusion and exclusion criteria for respondent selection.

  2. Identify the target population or universe.

  3. Identify the accessible respondent population.

  4. Specify the sampling design and sample size calculation.

  5. Recruit the subjects according to ethical and methodological guidelines.

• Sampling Methods Taxonomy

  • Non-Probability Sampling: Subjects selected via non-random methods; cannot estimate selection probability for each element.

    • Accidental or Convenience Sampling: Selecting readily accessible, available subjects.

    • Quota Sampling: Selecting subjects non-randomly according to pre-established subgroup proportions (e.g., specific target numbers for males over age 40).

    • Purposive or Judgment Sampling: Subjective selection of participants based on researcher expertise regarding who holds relevant knowledge.

    • Snowball / Network / Chain Sampling: Early respondents refer additional acquaintances matching study criteria.

    • Modal Instance Sampling: Sampling individuals representing the typical case of a population distribution.

    • Advantages: Convenient, fast, economical, requires no fixed pre-determined budget.

    • Disadvantages: Susceptible to sampling bias, errors in judgment, inability to calculate precise sampling error, unequal selection opportunities.

  • Probability Sampling: Uses random selection ensuring every element in the target population has an equal, independent chance of selection, minimizing bias.

    • Simple Random Sampling: Pure random selection using methods like tables of random numbers or lottery systems.

    • Stratified Random Sampling: Population divided into homogenous strata, followed by random sampling within each stratum.

    • Cluster / Multi-Stage Sampling: Population divided into geographic or structural clusters; clusters are randomly chosen before sampling within them.

    • Systematic / Sequential Sampling: Selecting every $n\text{th}$ element from a sampling frame after a random start.

    • Advantages: Dramatically reduces bias; enables statistical generalizability.

    • Disadvantages: Time-consuming, expensive, logistically complex, occasionally impossible to obtain complete sampling frames.

  • Theoretical Sampling:

    • Coined by Barney Glaser and Anselm Strauss (1967) within Grounded Theory methodology.

    • Data collection driven by emerging categories and theoretical concepts rather than population generalizability.

    • Functions as a technique for data triangulation using independent information units to clarify partially understood phenomena; colloquially referred to as "handy sampling."

• Importance of Rigorous Sampling

  • Ensures subject quality via strict inclusion/exclusion parameters.

  • Clarifies study boundaries and scope limitations.

  • Maximizes time and resource efficiency.

  • Safeguards data quality by helping control or eliminate extraneous confounding variables.

  • Addresses economic and financial resource constraints.

Theoretical and Conceptual Frameworks

• Nature and Purpose of Research Frameworks

  • A framework provides the overall structure or conceptual underpinnings for an argument supporting the study's rationale and research question.

  • Justifies the study rationale, explains reasons for analyzing data, and specifies underlying theoretical relationships.

• Theoretical vs. Conceptual Frameworks

• Theoretical Concepts and Etymology

  • Etymology: Derived from the Greek word theoria, meaning "a beholding or speculation" (Nieswiadomy, 1998).

  • Theories represent abstract explanations and generalizations systematically detailing relationships between phenomena to describe, predict, explain, and control them (Abdellah, 2002).

  • Theories are NEVER PROVED; they remain open to refinement, modification, or rejection if unconfirmed by empirical observations (Nieswiadomy, 1998).

  • Provide professional autonomy and power by guiding practice, education, and research, improving clinical decision-making, and sharpening analytical skills (Polit & Beck, 2008).

• Four Levels / Purposes of Theories (Levels of Inquiry)

  1. Factor Isolating: Categorizes and describes phenomena.

  2. Factor Relating: Explains relationships among phenomena.

  3. Situation Relating: Predicts explicit interrelationships between variables.

  4. Situation Producing: Controls phenomena and directs relationships toward desired clinical outcomes.

• Structural Elements of Frameworks

  • Models: Symbolic, structural, pictorial, diagrammatic, or mathematical representations of concrete or abstract reality (Bush, 2002).

  • Constructs: Highly abstract, complex phenomena that cannot be directly observed but are inferred from concrete indicators (e.g., wellness, mental health, self-esteem, assertiveness, good health, nursing care).

  • Paradigms: Global perspective representing core issues of interest to a profession. In nursing, major paradigms include behavioral, developmental, interactional, and systems-based models.

• Everyday Practical Application Scenario

  • Blizzard Scenario: Driving in an unexpected snowstorm with 20-minute traffic delays, witnessing erratic driving (too fast or extremely slow), reaching a store with limited inventory (few milk gallons, 15 egg cartons, smashed bread), and waiting in 10-12 person lines with crying children and anxious shoppers.

  • Application: An individual instinctively interprets and explains these behaviors by applying psychological and sociological stress theories. This illustrates that theories are practical mechanisms used universally across disciplines to explain and predict human behavior under stress.

• Inventory of Specific Nursing Theoretical Frameworks

  • Mishel (1988) – Uncertainty

  • Peplau (1988) – Interpersonal Theory

  • Pender (1987) – Health Promotion

  • King (1981) – Goal Attainment

  • Cox (1982) – Interaction Model of Client Behavior

  • Orem (1991) – Self-Care Deficit Theory

  • Rogers (1970) – Unitary Person

  • Roy (1984) – Adaptation

  • Neuman (1972) – Systems Model

• Inventory of Theoretical Frameworks from Other Disciplines

  • Bandura (1986); Rotter (1954) – Social Learning Theory

  • Knowles (1980) – Adult Learning Theory

  • Mead (1934) – Role Theory

  • Spielburg (1972) – State-Trait Anxiety

  • Selye (1976) – Stress

  • Seligman (1975) – Helplessness

  • Festinger (1957) – Cognitive Dissonance

  • Piaget (1926); Freud (1938); Erickson (1950); Havighurst (1952) – Developmental Theory

  • Maslow (1970) – Motivation

  • Caplan (1964) – Crisis

  • Benson (1975) – Relaxation

  • Melzak and Wall (1983) – Pain / Gate Control Theory

  • Schilder (1952) – Body Image

  • Herzberg (1966) – Job Satisfaction

  • Minuchin (1974); Duvall (1977) – Family Theory

  • Satir (1967) – Family Communication

  • Lazarus and Folkman (1984) – Coping

  • Kohlberg (1978) – Moral Reasoning

  • Lewin (1951) – Change Theory

  • Becker (1985) – Health Behaviors

  • Becker (1955) – Health Belief

  • Fishbein & Ajzen (1975) – Attitudes

• Step-by-Step Procedure for Implementing a Theoretical Framework

  1. Perform a literature search to examine candidate theories for the research problem.

  2. Select the specific theory that will guide the entire study.

  3. Conduct a comprehensive review of literature focusing on studies that utilized this selected theory.

  4. Formulate research questions or hypotheses derived directly from theoretical propositions.

  5. Draft conceptual definitions for study variables based on theoretical constructs.

  6. Operationalize study variables by selecting measurement instruments congruent with the theory.

  7. Explain study findings using the underlying theoretical constructs.

  8. Draw conclusions grounded directly in the framework.

  9. Evaluate whether the empirical findings support or challenge the theory.

  10. Identify clinical and theoretical implications of the findings.

  11. Formulate recommendations for future research incorporating the theoretical framework.

Research Variables and Operationalization

• Definition and Function of Variables

  • A variable is any measurable characteristic, property, attribute, or phenomenon that varies among individuals, objects, or situations.

  • Serves as the foundation for research problems, hypothesis testing, and statistical analysis.

  • Purposely designed by researchers to solve targeted research questions.

• Classification of Variables

  • Explanatory Variables:

    • Independent Variable (IV): The presumed cause, antecedent, or predictor variable manipulated or observed by the researcher.

    • Dependent Variable (DV): The outcome, effect, or criterion variable measured to determine response to the IV.

    • Intervening Variable: A variable that operates between the IV and DV, transmitting the impact of the cause to the outcome.

    • Mediator Variable: Explains the underlying pathway or mechanism through which the IV influences the DV.

    • Moderator Variable: A variable that alters the strength or direction of the statistical relationship between the IV and DV.

  • Extraneous or Exogenous Variables: Uncontrolled variables that can confound results.

    • Organismic Variables: Internal characteristics of subjects (e.g., age, physiological status).

    • Environmental Variables: External setting characteristics (e.g., room temperature, ambient noise).

  • Abstract or Continuous Variables: Variables that can take on an infinite continuum of fractional values (e.g., weight, anxiety score).

  • Dichotomous Variables: Categorical variables restricted to exactly two mutually exclusive outcomes (e.g., yes/no, male/female).

  • Active Variables: Variables that can be manipulated experimentally by the researcher (e.g., dosage, intervention type).

  • Attribute Variables: Pre-existing, innate subject characteristics that cannot be manipulated (e.g., blood type, age).

• Concrete Relationship Illustration

  • Sample Research Title: "Mediating Role of Cultural Assessment on the Relationship Between Adversity Quotient and Work Readiness"

    • Independent Variable (IV): Adversity Quotient

    • Mediator Variable: Cultural Assessment

    • Dependent Variable (DV): Work Readiness

  • Diagrammatic Flow: [Adversity Quotient] → [Cultural Assessment] → [Work Readiness]

• Defining Variables for Operationalization

  • Conceptual Definition: Theoretical definition derived directly from academic literature.

  • Operational Definition: Precise specification of how a variable will be empirically measured, observed, or manipulated within a specific study.

  • Key Components of an Operationalization Matrix:

    1. Variable Name: Identifier of the construct.

    2. Type: IV, DV, Mediator, Moderator, or Control.

    3. Conceptual Definition: Literature-based description.

    4. Operational Definition: Exact measurement process.

    5. Instrument/Scale: Specific measurement tool used.

    6. Level of Measurement: Nominal, Ordinal, Interval, or Ratio classification.