skills/43-wentorai-research-plugins/skills/research/deep-research/meta-synthesis-guide/SKILL.md
Conduct qualitative meta-synthesis and evidence synthesis methods
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research meta-synthesis-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A skill for conducting qualitative meta-synthesis -- the systematic integration of findings across multiple qualitative studies. Covers meta-ethnography (Noblit & Hare), thematic synthesis (Thomas & Harden), framework synthesis, and quality appraisal of qualitative studies.
Meta-synthesis is to qualitative research what meta-analysis
is to quantitative research -- it systematically combines
findings from multiple studies to produce higher-order
interpretations.
Key differences from meta-analysis:
- Interpretive, not statistical aggregation
- Aims to generate new understanding, not average effect sizes
- Synthesizes themes, concepts, and metaphors across studies
- Product is a new interpretation, not a pooled statistic
Appropriate when:
- Multiple qualitative studies exist on a topic
- You want to build theory or deepen understanding
- Individual studies have limited scope but collectively cover a phenomenon
- Policy or practice needs an integrated evidence base from qualitative work
Not appropriate when:
- Studies are too heterogeneous in topic to meaningfully compare
- Fewer than 3 qualitative studies exist
- The goal is to measure effect sizes (use meta-analysis instead)
def meta_ethnography_steps() -> dict:
"""
The seven steps of meta-ethnography (Noblit & Hare, 1988).
"""
return {
"step_1_getting_started": {
"description": "Identify the research question and intellectual interest",
"output": "Clear synthesis question"
},
"step_2_deciding_what_is_relevant": {
"description": "Systematic search and selection of qualitative studies",
"output": "Final set of included studies",
"note": "Use PRISMA flow diagram to document selection"
},
"step_3_reading_the_studies": {
"description": (
"Read and re-read included studies carefully. "
"Identify key metaphors, themes, and concepts in each."
),
"output": "List of first-order (participant quotes) and "
"second-order (author interpretations) constructs"
},
"step_4_determining_how_studies_are_related": {
"description": (
"Create a grid mapping constructs across studies. "
"Determine if studies are reciprocal (about similar things), "
"refutational (contradictory), or form a line of argument."
),
"output": "Construct comparison table"
},
"step_5_translating_studies": {
"description": (
"Translate the concepts of one study into the terms of another. "
"This is the core analytical step -- finding common meaning "
"expressed in different language."
),
"output": "Translated constructs across all studies"
},
"step_6_synthesizing_translations": {
"description": (
"Develop third-order constructs -- new interpretations "
"that go beyond what any single study found."
),
"output": "Third-order constructs (the synthesis)"
},
"step_7_expressing_the_synthesis": {
"description": "Write up the synthesis in a form accessible to the audience",
"output": "Published meta-synthesis paper"
}
}
Reciprocal translation:
Studies are about similar things. Translate them into each other.
"Study A calls it 'navigating uncertainty'; Study B calls it
'managing ambiguity'; Study C calls it 'living with not knowing'.
The overarching construct is 'Tolerating the Unknown.'"
Refutational synthesis:
Studies contradict each other. Explore why.
"Study A found empowerment through peer support; Study B found
peer support increased anxiety. This refutation may be explained
by the stage of illness at which support was received."
Line of argument synthesis:
Studies address different aspects that together form a whole.
"Study A covers diagnosis, B covers treatment, C covers recovery.
Together they reveal a trajectory of 'Reconstructing Identity.'"
Stage 1: Free coding of findings
- Treat the findings sections of included studies as data
- Code them line by line, as in primary qualitative analysis
Stage 2: Organizing codes into descriptive themes
- Group codes into descriptive themes
- These are "close to" the original studies
Stage 3: Generating analytical themes
- Go beyond the content of the original studies
- Generate new interpretive constructs
- Answer the synthesis research question
Tools for appraising qualitative study quality:
CASP Qualitative Checklist (10 items):
- Was there a clear statement of aims?
- Is a qualitative methodology appropriate?
- Was the research design appropriate?
- Was the recruitment strategy appropriate?
- Was data collected in a way that addressed the research issue?
- Was the relationship between researcher and participants considered?
- Were ethical issues considered?
- Was data analysis sufficiently rigorous?
- Was there a clear statement of findings?
- How valuable is the research?
JBI Checklist for Qualitative Research (10 criteria)
Decision: Include all studies or exclude low-quality studies?
- Sensitivity analysis: Run the synthesis with and without
lower-quality studies to see if conclusions change.
Use the ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) statement. Report: the synthesis methodology used, the search strategy and selection criteria, quality appraisal results, a table of included studies with their key constructs, the synthesis process with clear evidence trails, and how third-order constructs were derived from the primary studies.
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.