Patient's Experience in Emergency Care

Patient's Experience in Emergency Centre

• The patient felt abandoned in the emergency centre.
• They were pushed to the center of the room, left alone without assistance or explanation.
• The patient expressed feeling dismissed.
• The care provider did not communicate with the patient once they had been placed in the room.

Defining a Category

• Categories group codes with similarities based on content or context.
• Categories should answer who, what, when, or where and are based on manifest content.
• Category names should be factual and succinct.

Formulating a Category

• First create sub-categories - groups of related codes.
• Sub-categories with similar content are combined into categories.

Theme

• Themes represent recurring patterns across a dataset, highlighting central concepts.
• Themes offer interpretations of multiple categories and are expressed on a latent level.
• They answer questions like why, how, in what way, or by what means.
• Themes are descriptive and use verbs, adverbs, and adjectives.

Code Book

• Keep a record of all codes, their descriptions, and examples in a codebook for easy reference.

Collaborative Coding and Member Checks

• Multiple researchers can code the same data to ensure interpretive convergence.
• Researchers can involve research subjects in coding, either through collaborative coding or member checks.
• Member checks increase the validity of knowledge claims.

Data Analysis: Pantoum Poem

• A pantoum is a poem where stanzas have four lines.
• The second and fourth lines of each stanza are repeated as the first and third lines of the next stanza.
• Choose six striking utterances from your data for your pantoum poem.

Individual Activities

• Choose two pictures, two phrases, or two words that represent you.
• Write a paragraph or two about who you are.
• Highlight six lines of the text that express who you are.
• Create a pantoum poem from data, selecting six utterances that are prominent.
• Present your pantoum poem in a 15-minute individual presentation.

Types of Data Analysis

• Content analysis focuses on identifying words, themes, or concepts within qualitative data
• Thematic analysis involves identifying, analyzing, and interpreting patterns within qualitative data to find themes
• Narrative analysis interprets core narratives from personal stories within a study group, using first-person accounts to understand individual experiences
• Grounded theory analysis aims to discover or construct theories from data
• Discourse analysis is an interpretive method of analyzing texts, considering both details and contextual knowledge

Patterns in Data

• Patterns can be characterized by similarity, difference, frequency, sequence, correspondence, and causation
• Data can be characterized by "fuzzy boundaries"
• Codes emerge not only from expected patterns, but also from surprising, unusual, or conceptually captivating aspects of the data

Deductive Approach to Data Analysis

• Deductive approaches start with a research question and use specific extracts to find evidence to support the answer

Thematic Analysis

• Identifies, analyzes, and reports patterns (themes) within data
• Provides a qualitative, detailed, and nuanced account of data
• Involves searching for common threads across interviews or sets of interviews
• Is a method, flexible and adaptable to different epistemological perspectives

Semantic and Latent Analysis

• Semantic analysis focuses on the explicit or surface meanings of data
• Latent analysis looks beyond the surface meaning to examine underlying ideas, assumptions, and ideologies
• Both semantic and latent analysis are used to understand data in the context of thematic analysis

Top-Down and Bottom-Up Thematic Analysis

• Top-down thematic analysis is driven by research questions and the analyst's focus
• Bottom-up thematic analysis is driven by the data itself

Braun & Clarke's Six-Phase Framework for Thematic Analysis

• Step 1: Become familiar with the data through reading and re-reading transcripts, making notes, and jotting down early impressions
• Step 2: Generate initial codes through line-by-line coding to code every single line, or aspects of the data that are deemed salient
• Step 3: Search for themes by grouping codes into categories that share common elements
• Step 4: Review themes by refining and adjusting them based on the data
• Step 5: Define themes by giving them clear and concise labels
• Step 6: Write-up by writing a report summarizing the identified themes, explanations, and examples

Coding Methods

• Simultaneous Coding: Applying two or more codes to a single datum, highlighting interconnected elements
• Values Coding: Capturing and labeling subjective-value perspectives within the data
• Holistic or "Lumper" Coding: Using one code to represent an entire excerpt, capturing its essence
• Process Coding: Using a word or phrase to capture action or behavior within the data
• Descriptive Coding: Summarizing the primary topic of an excerpt or datum
• In Vivo Code: Using a verbatim phrase from the data as a code, often in quotation marks
• Initial Coding: An open-ended approach where the researcher codes their first impressions of the data
• Splitter Coding: Breaking data into smaller parts to utilize multiple codes, contrasting with Holistic or "Lumper" coding

Questions for Coding Field Notes

• What are people doing?
• What are they trying to accomplish?
• How exactly do they do this?
• What specific means and/or strategies do they use?
• How do members talk about, characterize, and understand what is going on?
• What assumptions are they making?
• What do I see going on here?
• What did I learn from these notes?
• Why did I include them?

Coding Activity

• “They pushed me into the middle of the room and then walked away...they just left me."
  • Code: Left alone
  • Category: Staff action and non-action; unmet need; lack of communication; lack of professionalism
  • Theme: Not a person, just a body in the hectic EC, dehumanizing care in the EC

Keeping a Codebook

• Maintaining a record of emergent codes, descriptions, and data examples in a codebook, separate file, or through qualitative analysis software helps organize and manage the coding process

Definition of a Category

• A category describes the aspects, similarities or differences in the content that belong together

Formulating a Category

• Assimilate smaller groups of closely related codes into sub-categories
• Group sub-categories together based on related content
• Categories answer questions about who, what, when, or where
• Categories are an expression of manifest content, i.e., what is visible and obvious in the data
• Category names are factual and short

Theme Definition

• Themes capture patterns across a dataset, organized around a central idea
• Themes express an underlying meaning, i.e., latent content, found in two or more categories
• Themes answer questions such as why, how, in what way, or by what means
• Theme names are descriptive and include verbs, adverbs, and adjectives

Theme Example

• Data: “They pushed me into the middle of the room and then walked away … they just left me”
• Theme: The emergency centre through patients' eyes- Alone and cold in chaos
• Theme: Not a person, just a body in the hectic EC, dehumanizing care in the EC

Theme Identification

• Identifying themes helps researchers go beyond the literal content of data and understand the deeper meaning of the patterns
• By identifying themes, researchers can develop a more nuanced understanding of the data.

Thematic Analysis

• A method used to identify, analyze, and report patterns within data.
• Provides a qualitative, detailed, and nuanced account of data.
• Involves the search for common trends across an entire interview or set of interviews.

Thematic Analysis as a Method

• A method, not a methodology.
• Not tied to a particular epistemological or theoretical perspective.
• Flexible method suitable for diverse areas of study.

Semantic and Latent Analysis

• Semantic themes: Focus on the explicit or surface meanings of the data.
• Latent level: Looks beyond the surface meaning to identify underlying ideas, assumptions, and ideologies.

Top-down and Bottom-up Thematic Analysis

• Top-down or theoretical: Driven by the specific research question(s) and/or the analyst’s focus.
• Bottom-up or inductive: Driven by the data itself.

Braun & Clarke’s Six-Phase Framework for Thematic Analysis

• Step 1: Become familiar with the data:
  • Reading and re-reading transcripts.
  • Making notes and jotting down early impressions.
• Step 2: Generate initial codes:
  • Line-by-line coding to code every single line.
• Step 3: Search for themes:
  • No hard and fast rules about what makes a theme.
  • A theme is characterized by its significance.
• Step 4: Review themes:
  • Gather all data relevant to each theme.
  • Assess if themes make sense, if data supports them, and if there are overlapping themes.
• Step 5: Define themes:
  • Final refinement of themes.
  • Identify the 'essence' of each theme.
  • Explain how themes relate to each other.
• Step 6: Write-up:
  • The end-point of research is usually a report.

Content Analysis

• Used to determine trends and patterns of words used, their frequency, relationships, and the structures and discourses of communication.
• Examines who says what, to whom, and with what effect.

Codifying and Categorizing

• Codify: To arrange things in a systematic order, categorize, or make something part of a system.
• Categories: Generated by grouping similarly coded data based on shared characteristics.
• Categories can be descriptive or more conceptual, depending on the method.

Themes

• Themes: Capture a common, recurring pattern across a dataset, organized around a central organizing concept.
• Themes describe the different facets of a pattern across the dataset and express underlying meaning (latent content).
• Themes answer questions like why, how, in what way, or by what means.
• Theme names are descriptive and use verbs, adverbs, and adjectives.

Keeping a Codebook

• It's recommended to keep a record of codes, their content descriptions, and a brief data example in a codebook.

Collaborative Coding and Member Checks

• Multiple researchers can code the same raw data and attempt to bridge or synthesize divergences.
• Researchers can involve study subjects in the coding/analytical process to increase the validity of knowledge claims.

Data analysis: Pantoum poem

• A pantoum poem is a form of poetry where the second and fourth lines of each stanza are repeated as the first and third lines of the next stanza.
• The final line of the poem is often the same as the first line of the first stanza.

Data presentation: How to write a pantoum poem

• The author provides a simplified guide for writing a pantoum poem.