thematic-analysis

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主题分析辅助工具（Thematic Analysis）

Thematic Analysis Assistant Tool

本 skill 基于 Braun & Clarke（2006, 2019）的反思性主题分析框架，支持从原始访谈文本到候选主题结构的完整分析流程。

重要定位：主题命名是分析行为，体现研究者的理论判断，最终命名必须由研究者作出。本 skill 在命名阶段只提供备选，不做裁定。

方法论前提：Braun & Clarke 的反思性 TA 要求研究者对所有访谈逐份独立完成初始编码，再将全部编码汇总为统一的编码池，然后才进入主题搜寻阶段。

This skill is based on Braun & Clarke's (2006, 2019) Reflexive Thematic Analysis framework, supporting the complete analysis process from raw interview text to candidate theme structure.

Important Positioning: Theme naming is an analytical act that reflects the researcher's theoretical judgment, and the final naming must be determined by the researcher. This skill only provides alternatives during the naming stage, not making decisions.

Methodological Premise: Braun & Clarke's Reflexive TA requires researchers to independently complete initial coding for all interviews one by one, then aggregate all codes into a unified coding pool before entering the theme searching stage.

启动：确认输入类型

Initiation: Confirm Input Type

触发后，第一步必须确认输入类型：

"你现在准备做主题分析——你手上有的是： A. 原始访谈文本（尚未编码） B. 已完成的初始编码（一份或多份）哪种情况？"

情况 A：提供原始访谈文本 → 进入初始编码阶段

情况 B：提供已有初始编码 → 询问是否已汇总，进入编码池处理

After triggering, the first step must be to confirm the input type:

"You are now ready to conduct thematic analysis—what you have is: A. Raw interview text (not yet coded) B. Completed initial coding (one or multiple sets) Which scenario applies?"

Scenario A: Provide raw interview text → Enter initial coding stage

Scenario B: Provide existing initial coding → Ask if it has been aggregated, then proceed to coding pool processing

编码风格确认（情况 A 专用，可选）

Coding Style Confirmation (Exclusive to Scenario A, Optional)

确认为情况 A 后，在正式开始编码前，询问研究者是否希望提供示范编码：

"在正式编码前，你可以选择： A. 研究者示范：你先对文本中任意一小段（3–5 句）做示范编码，AI 会识别你的编码风格，然后按你的风格完成后续编码 B. AI 直接编码：跳过示范，AI 按照标准原则直接开始

哪种方式？（选 B 或不回应则直接开始）"

若研究者选择 A（研究者示范）：

请研究者提供示范片段及其对应编码（格式：
```
"原文" → [编码标签]
```
）
AI 分析示范编码的风格特征，明确说明：
- 粒度：偏细（逐句）还是偏粗（逐段）？
- 用词：in-vivo（受访者原话）为主，还是研究者概括语言为主？
- 长度：编码标签通常几个字？
- 描述取向：倾向描述行为，还是描述情绪/态度？
输出风格确认：

"我理解你的编码风格是：[描述]。我将按照此风格完成后续编码。如有偏差，请随时纠正。"
按研究者风格继续执行步骤 1–2.5

若研究者选择 B 或未回应：

直接进入步骤 1，按标准原则编码。

After confirming Scenario A, before officially starting coding, ask the researcher if they want to provide a demonstration coding:

"Before officially starting coding, you can choose: A. Researcher Demonstration: You first conduct demonstration coding on any small segment (3–5 sentences) of the text, and the AI will identify your coding style, then complete the subsequent coding according to your style B. AI Direct Coding: Skip the demonstration, and the AI will start directly according to standard principles

Which option? (Choose B or no response will start directly)"

If the researcher chooses A (Researcher Demonstration):

Ask the researcher to provide the demonstration segment and its corresponding coding (format:
```
"Original text" → [coding tags]
```
)
The AI analyzes the stylistic characteristics of the demonstration coding, clearly explaining:
- Granularity: Fine-grained (sentence-by-sentence) or coarse-grained (paragraph-by-paragraph)?
- Wording: Dominated by in-vivo (respondents' original words) or researchers' summarized language?
- Length: How many words are usually in coding tags?
- Descriptive orientation: Tend to describe behaviors, or emotions/attitudes?
Output style confirmation:

"I understand your coding style is: [description]. I will complete the subsequent coding according to this style. Please correct me at any time if there are deviations."
Continue to execute steps 1–2.5 according to the researcher's style

If the researcher chooses B or does not respond:

Directly enter step 1 and code according to standard principles.

初始编码阶段（情况 A）

Initial Coding Stage (Scenario A)

TA 的初始编码与扎根理论的开放编码有本质差异：

	TA 初始编码	GT 开放编码
目标	捕捉意义单元，贴近数据语言	为类属建构准备，需要概念抽象
粒度	短语级，尽量用受访者原话	可更高度概括
后续	汇总后搜寻主题（并列结构）	归并类属、属性维度分析（层级结构）

There are essential differences between TA's initial coding and grounded theory's open coding:

	TA Initial Coding	GT Open Coding
Goal	Capture meaning units, close to data language	Prepare for category construction, requires conceptual abstraction
Granularity	Phrase-level, use respondents' original words as much as possible	Can be more highly generalized
Follow-up	Search for themes after aggregation (parallel structure)	Merge categories, analyze attribute dimensions (hierarchical structure)

单份访谈编码操作

Single Interview Coding Operation

研究者提供原始访谈文本后，执行以下步骤：

步骤 1：通读全文，识别意义单元

逐句扫描全文。只有以下两类才可跳过，且必须在编码结果中标注"已跳过"：

纯粹的单词应答，独立成句（如仅有"嗯""对""好的"）
访谈者的提问语句本身（非受访者发言）

其余所有语句，无论看起来信息量多少，都作为意义单元处理并给出编码。某句话是否重要，是研究者的判断权，不是 AI 的判断权。当不确定时，给出一个描述性编码（如"重复前述观点""表达不确定"），而不是跳过。

步骤 2：逐单元生成初始编码

编码原则：

贴近数据：编码词汇尽量来自受访者的语言，而非研究者的理论术语
描述性：编码描述"发生了什么"或"受访者表达了什么"，不解释"为什么"
细粒度：一个意义单元只给一个最准确的编码，不做归并
in-vivo 优先：若受访者某个表达特别精准，直接用原话作为编码
禁止对仗整齐：编码标签的字数和句法结构应由数据内容决定，而非由输出形式决定。in-vivo 编码可长可短，描述性编码因意义单元的复杂程度而异。如果回头检查发现大多数编码字数相近、结构相同（如全部是"X的Y"或"对X的Z"），说明在优化形式而非忠实于数据，必须主动打破这种整齐感，让编码长短形态反映数据本身的多样性。

输出格式：

【访谈 N】初始编码
（被访者简称 / 编号）

原文片段 → 编码
"......" → [编码标签]
"......" → [编码标签]
...

本份编码总数：N 条

步骤 2.5：自动保存编码结果到文件（强制执行，不可跳过）

编码输出完成后，必须立即调用 Write 工具将编码写入文件。不得仅在对话中输出而不写文件。

文件命名规则：

coding_[被访者编号或简称].md

（如

coding_A.md

、

coding_P1.md

）

保存路径：当前工作目录（即项目目录根目录）

文件内容格式：

undefined

After the researcher provides the raw interview text, execute the following steps:

Step 1: Read the full text, identify meaning units

Scan the full text sentence by sentence. Only the following two types can be skipped, and must be marked as "Skipped" in the coding results:

Pure single-word responses that form independent sentences (e.g., only "Hmm", "Yes", "Okay")
The interviewer's question sentences themselves (not the interviewee's statements)

All other sentences, regardless of how much information they seem to contain, are treated as meaning units and coded. Whether a sentence is important is the researcher's judgment, not the AI's. When uncertain, provide a descriptive code (e.g., "Repeat previous view", "Express uncertainty") instead of skipping.

Step 2: Generate initial codes unit by unit

Coding principles:

Close to data: Coding vocabulary should come from the respondents' language as much as possible, rather than the researcher's theoretical terms
Descriptive: Codes describe "what happened" or "what the respondent expressed", not explaining "why"
Fine-grained: Assign only one most accurate code to each meaning unit, do not merge
In-vivo priority: If a respondent's expression is particularly precise, directly use the original words as the code
Avoid uniform structure: The number of words and syntactic structure of coding tags should be determined by the content of the data, not the output form. In-vivo codes can be long or short, and descriptive codes vary depending on the complexity of the meaning unit. If a review finds that most codes have similar word counts and identical structures (e.g., all are "Y of X" or "Z of X"), it means you are optimizing form rather than being faithful to the data. You must actively break this uniformity, allowing the length and form of codes to reflect the diversity of the data itself.

Output format:

【Interview N】Initial Coding
(Respondent abbreviation / ID)

Original text segment → Code
"......" → [coding tag]
"......" → [coding tag]
...

Total codes in this document: N

Step 2.5: Automatically save coding results to file (Mandatory, cannot be skipped)

After completing the coding output, must immediately call the Write tool to write the codes to a file. Do not only output in the dialogue without writing to a file.

File naming rule:

coding_[respondent ID or abbreviation].md

(e.g.,

coding_A.md

coding_P1.md

)

Save path: Current working directory (i.e., root directory of the project directory)

File content format:

undefined

【访谈 X】初始编码

【Interview X】Initial Coding

被访者：[编号/简称] 编码日期：[日期]

Respondent: [ID/abbreviation] Coding date: [date]

编码列表

Coding List

"原文片段" → [编码标签] "原文片段" → [编码标签] ...

"Original text segment" → [coding tag] "Original text segment" → [coding tag] ...

统计

Statistics

本份编码总数：N 条


Write 工具调用完成后，告知研究者：
> "本份编码已通过 Write 工具保存至 `coding_[被访者编号].md`。"

**步骤 3：询问是否继续下一份**

> "【访谈 N】初始编码完成，共 N 条。
> 还有下一份访谈文本吗？如有，请提供；
> 如果这是最后一份，我们进入编码汇总。"

所有访谈编码完成后，自动进入辅助汇总流程。

---

Total codes in this document: N


After completing the Write tool call, inform the researcher:
> "This coding has been saved to `coding_[respondent ID].md` via the Write tool."

**Step 3: Ask if to proceed to the next interview**

> "【Interview N】Initial coding completed, total of N codes.
> Do you have the next interview text? If yes, please provide it;
> If this is the last one, we will proceed to code aggregation."

After completing coding for all interviews, automatically enter the assistant aggregation process.

---

编码池处理（情况 B）

Coding Pool Processing (Scenario B)

研究者已有初始编码时，询问汇总状态：

"你已有的初始编码来自几份访谈？是否已经汇总到一份列表里？"

已汇总 → 直接进入后续信息收集

未汇总 → 执行辅助汇总

研究者依次提供各份访谈的编码：

【访谈 N】（被访者简称或编号）
编码1
编码2
...

When the researcher has existing initial codes, ask about the aggregation status:

"How many interviews do your existing initial codes come from? Have they been aggregated into a single list?"

Already aggregated → Directly enter subsequent information collection

Not aggregated → Execute assistant aggregation

The researcher provides the codes for each interview in sequence:

【Interview N】(Respondent abbreviation or ID)
Code 1
Code 2
...

辅助汇总

Assistant Aggregation

无论来自情况 A 还是情况 B，所有编码汇总后输出：

汇总编码池
总编码数：N 条
来源访谈：P1, P2, ... PN

跨访谈重复出现的编码（出现 ≥2 次）：
- [编码名]：出现于 P1, P3
- ...

单份访谈独有的编码：
- [编码名] [P2]
- ...

汇总完成后，收集后续必要信息：

必填：研究问题——用一句话说明研究在问什么，是判断主题相关性的基本参照。

选填：理论视角——若有，在主题审查阶段提示主题与理论的对话关系。

选填：当前困惑——若对某些编码归属已有疑虑，先行标注，优先处理。

Regardless of coming from Scenario A or B, after aggregating all codes, output:

Aggregated Coding Pool
Total number of codes: N
Source interviews: P1, P2, ... PN

Codes repeated across interviews (appeared ≥2 times):
- [Code name]: Appeared in P1, P3
- ...

Codes unique to a single interview:
- [Code name] [P2]
- ...

After completing aggregation, collect the following necessary information:

Required: Research question — Explain in one sentence what the research is asking, which is the basic reference for judging theme relevance.

Optional: Theoretical perspective — If available, prompt the dialogue relationship between themes and theories during the theme review stage.

Optional: Current confusion — If there are already doubts about the attribution of certain codes, mark them in advance and prioritize processing.

执行流程

Execution Process

确认编码池完整后，自动连续执行以下四个阶段，无需每步等待用户确认。

After confirming the coding pool is complete, automatically and continuously execute the following four stages, no need to wait for user confirmation at each step.

第一阶段：编码全貌扫描

Stage 1: Coding Overview Scan

在进入聚类之前，先对全部编码做一次整体扫描，输出：

编码总数
编码的大致分布特征（哪些概念域出现频繁）
初步识别出的"编码聚集带"（尚未命名的雏形聚类）
标记出你认为"孤立编码"（与其他编码缺少关联的）

这一步的目的是让研究者在进入正式聚类前，先看到编码的整体地形。

Before entering clustering, first conduct an overall scan of all codes, outputting:

Total number of codes
General distribution characteristics of codes (which conceptual domains appear frequently)
Initially identified "code clusters" (unnamed prototype clusters)
Marked "isolated codes" (lacking association with other codes)

The purpose of this step is to let the researcher see the overall landscape of the codes before entering formal clustering.

第二阶段：候选主题聚类

Stage 2: Candidate Theme Clustering

将编码归入候选主题，输出 5–8 个候选主题（若编码数量极少可减少）。

聚类原则：

语义相关性优先：同一主题内的编码应指向同一类经验或意义
不强行归并：宁可留出"边界模糊编码"，也不牵强归类
不以频率决定主题：高频编码不等于独立主题，低频编码可能是重要主题
允许层级结构：若某主题内部明显可分为两个子方向，可提出主题+子主题

每个候选主题的输出格式：

候选主题 [序号]：[暂定名称（描述性，非最终）]

核心含义：用1-2句话说明这个主题在捕捉什么经验或意义

包含编码：
- [编码1]
- [编码2]
- ...

边界模糊编码（归属不确定，需研究者判断）：
- [编码X]：模糊原因
- [编码Y]：模糊原因

Assign codes to candidate themes, output 5–8 candidate themes (can be reduced if the number of codes is extremely small).

Clustering principles:

Semantic relevance first: Codes within the same theme should point to the same type of experience or meaning
Do not force merging: It is better to leave "codes with ambiguous boundaries" than to force classification
Frequency does not determine themes: High-frequency codes do not equal independent themes, low-frequency codes may be important themes
Allow hierarchical structure: If a theme can clearly be divided into two sub-directions internally, propose theme + sub-theme

Output format for each candidate theme:

Candidate Theme [Number]: [Tentative name (descriptive, not final)]

Core meaning: Explain in 1-2 sentences what experience or meaning this theme captures

Included codes:
- [Code 1]
- [Code 2]
- ...

Codes with ambiguous boundaries (attribution uncertain, need researcher judgment):
- [Code X]: Reason for ambiguity
- [Code Y]: Reason for ambiguity

第三阶段：主题审查

Stage 3: Theme Review

对每个候选主题从以下三个维度进行审查，输出审查意见：

Review each candidate theme from the following three dimensions, outputting review opinions:

内部一致性

Internal Consistency

这个主题内部的编码，是否都在描述同一种经验或意义？还是有些编码其实属于不同的现象被勉强归在一起？

Do all codes within this theme describe the same type of experience or meaning? Or are some codes actually describing different phenomena that were reluctantly grouped together?

外部区分度

External Distinctiveness

这个主题与其他候选主题之间的边界是否清晰？如果有两个主题高度重叠，指出它们的差异在哪里，以及是否应该合并或拆分。

Is the boundary between this theme and other candidate themes clear? If two themes are highly overlapping, point out their differences and whether they should be merged or split.

与研究问题的相关性

Relevance to Research Question

这个主题是否真正在回应研究问题？还是它只是材料中的一个高频现象，但与研究问题关系较弱？

审查结果中，对每个候选主题给出明确判断：

保留：内部一致、外部清晰、与研究问题相关
建议合并：与某主题高度重叠，说明合并依据
建议拆分：内部包含两个不同方向，说明拆分依据
建议降级：可作为某主题的子主题，而非独立主题
存疑待判：内部一致性或相关性尚不确定，需研究者决定

备忘录提示（可选）： 主题审查过程中，如果某个主题让你产生了理论联想——觉得"这个主题和某个理论概念很像"或"这两个主题之间的张力说明了什么"——现在就可以切到
analytic-memo
，把这个想法写成分析备忘录。TA 的备忘录不需要遵循扎根理论的结构，直接说出你的想法即可。

Does this theme truly respond to the research question? Or is it just a high-frequency phenomenon in the materials but has a weak relationship with the research question?

In the review results, give a clear judgment for each candidate theme:

Retain: Internally consistent, externally distinct, relevant to the research question
Suggest merging: Highly overlapping with a certain theme, explain the basis for merging
Suggest splitting: Contains two different directions internally, explain the basis for splitting
Suggest downgrading: Can be a sub-theme of a certain theme rather than an independent theme
Pending judgment: Internal consistency or relevance is still uncertain, needs researcher decision

Memo Prompt (Optional): During the theme review process, if a theme triggers a theoretical association—such as thinking "this theme is very similar to a certain theoretical concept" or "the tension between these two themes explains something"—you can switch to
analytic-memo
now and write this idea as an analytic memo. TA memos do not need to follow grounded theory structures; just state your thoughts directly.

第四阶段：命名建议与移交

Stage 4: Naming Suggestions and Handover

这是 skill 移交给研究者的关键节点。

对每个通过审查的主题，提供：

This is the key node where the skill hands over to the researcher.

For each theme that passes the review, provide:

命名建议（2–3 个备选）

Naming Suggestions (2–3 alternatives)

每个备选名称附说明：

这个名称捕捉了什么？
它遗漏了什么？
它在理论上暗示了什么立场？

格式：

备选名称 A：[名称]
- 优点：...
- 局限：...
- 理论暗示：...

备选名称 B：[名称]
- 优点：...
- 局限：...
- 理论暗示：...

Each alternative name is accompanied by explanations:

What does this name capture?
What does it omit?
What theoretical stance does it imply?

Format:

Alternative Name A: [Name]
- Advantages: ...
- Limitations: ...
- Theoretical implication: ...

Alternative Name B: [Name]
- Advantages: ...
- Limitations: ...
- Theoretical implication: ...

理论定位追问

Theoretical Position Inquiry

在呈现完所有主题的备选名称后，输出以下追问：

命名背后的理论立场值得多停一步。 不同的备选名称往往暗示不同的理论对话方向—— 对于让你犹豫的主题，可以问自己：

这个名称是在描述现象，还是在解释机制？

它更靠近哪个已有的理论概念？这种靠近是你想要的吗？

如果换一个名称，你的研究会加入一场不同的理论对话——你想去哪场？

After presenting the alternative names for all themes, output the following inquiry:

It is worth pausing to consider the theoretical stance behind naming. Different alternative names often imply different theoretical dialogue directions— For themes that make you hesitate, you can ask yourself:

Is this name describing a phenomenon, or explaining a mechanism?

Which existing theoretical concept is it closer to? Is this proximity what you want?

If you choose a different name, your research will join a different theoretical conversation—where do you want to go?

移交声明

Handover Statement

在所有命名建议之后，输出以下固定文字：

命名判断移交研究者。 主题命名体现研究者的理论立场，以上备选名称仅供参考。以下问题请研究者自行判断后告知，以便进入后续工作：

哪些主题的名称已确定？

哪些主题的名称需要修改？

是否需要对某些主题进行合并、拆分或删除？

After all naming suggestions, output the following fixed text:

Naming judgment is handed over to the researcher. Theme naming reflects the researcher's theoretical stance, and the above alternative names are for reference only. Please inform after making your own judgments on the following questions to proceed to subsequent work:

Which theme names have been confirmed?

Which theme names need to be modified?

Do you need to merge, split or delete certain themes?

强制：主题汇总表自动保存

Mandatory: Automatic Saving of Theme Summary Table

研究者确认主题命名后，必须立即执行以下保存步骤（不可跳过）：

调用 Write 工具，将最终主题结构保存至项目目录：

文件命名：

themes_[研究主题].md

（如

themes_快车司机.md

、

themes_平台工人.md

文件内容：

研究问题（一句话）
主题结构表（含主题编号、名称、核心含义、包含编码数、代表性语句）
边界模糊编码的处理记录

Write 完成后告知研究者：

"
themes_[研究主题].md
已保存至项目目录，供后续
ta-methods-writer
、
ta-findings-writer
等 skill 自动读取。"

After the researcher confirms the theme naming, must immediately execute the following saving steps (cannot be skipped):

Call the Write tool to save the final theme structure to the project directory:

File naming:

themes_[research topic].md

(e.g.,

themes_taxi_drivers.md

themes_platform_workers.md

) Save path: Current working directory (project root directory)

File content:

Research question (one sentence)
Theme structure table (including theme number, name, core meaning, number of included codes, representative sentences)
Processing records of codes with ambiguous boundaries

After completing the Write operation, inform the researcher:

"
themes_[research topic].md
has been saved to the project directory, for automatic reading by subsequent skills such as
ta-methods-writer
and
ta-findings-writer
."

可选后续操作

Optional Follow-up Operations

研究者确认主题命名后，还可继续请求以下任意操作：

After the researcher confirms the theme naming, they can also request any of the following operations:

操作 A：生成主题结构摘要

Operation A: Generate Theme Structure Summary

输出一份完整的主题结构表，格式如下：

主题编号	主题名称	核心含义	包含编码数	代表性语句（若有原文）

Output a complete theme structure table in the following format:

Theme Number	Theme Name	Core Meaning	Number of Included Codes	Representative Sentences (if original text is available)

操作 B：追问特定主题

Operation B: Inquiry on Specific Themes

对研究者指定的某个主题，做更深入的分析：

这个主题在材料中是如何被体现的？
它内部是否存在张力或矛盾？
它与哪个理论概念最接近？距离在哪里？

Conduct a more in-depth analysis of a theme specified by the researcher:

How is this theme reflected in the materials?
Does it have internal tensions or contradictions?
Which theoretical concept is it closest to? What is the distance?

操作 C：识别主题之间的关系

Operation C: Identify Relationships Between Themes

分析候选主题之间是否存在：

因果或条件关系
对立或张力关系
层级或包含关系
时间序列关系

输出主题关系图谱（文字版）。

Analyze whether there are the following relationships between candidate themes:

Causal or conditional relationships
Opposing or tension relationships
Hierarchical or inclusive relationships
Time sequence relationships

Output a text-based theme relationship map.

操作 D：保存主题结构到本地

Operation D: Save Theme Structure to Local

将最终确认的主题结构保存为 Markdown 文件。

文件名格式：

YYYY-MM-DD_themes_<研究主题关键词>.md

默认保存路径：

~/Documents/research-memos/themes/

文件内容包含：研究问题、主题结构表、各主题含义说明、边界模糊编码处理记录。

Save the final confirmed theme structure as a Markdown file.

File name format:

YYYY-MM-DD_themes_<research topic keywords>.md

Default save path:

~/Documents/research-memos/themes/

File content includes: Research question, theme structure table, explanation of each theme's meaning, processing records of codes with ambiguous boundaries.

与其他 skill 的关系

Relationship with Other Skills

Skill	定位	何时使用
`thematic-analysis`	初始编码 + 主题识别与结构化	从原始访谈文本到候选主题的完整 TA 流程
`grounded-coding`	开放编码与类属建构（GT专用）	需要程序化扎根理论的系统编码与持续比较
`negative-case-finder`	反例与边界条件识别	主题确定后，挑战主题的普遍性
`analytic-memo`	分析备忘录（AI代写）	主题审查中产生分析直觉，需要快速记录

推荐流程：

text

thematic-analysis（逐份初始编码 → 汇总编码池 → 候选主题）
    ↓
negative-case-finder（挑战主题的边界）
    ↓
analytic-memo（深化核心主题的理论思考）

Skill	Positioning	When to Use
`thematic-analysis`	Initial coding + theme identification and structuring	Complete TA process from raw interview text to candidate themes
`grounded-coding`	Open coding and category construction (exclusive to GT)	Requires systematic coding and constant comparison for procedural grounded theory
`negative-case-finder`	Identification of counterexamples and boundary conditions	After themes are determined, challenge the universality of themes
`analytic-memo`	Analytic memo (AI-written)	Generate analytical intuitions during theme review, need to record quickly

Recommended process:

text

thematic-analysis (initial coding per document → aggregate coding pool → candidate themes)
    ↓
negative-case-finder (challenge theme boundaries)
    ↓
analytic-memo (deepen theoretical thinking on core themes)

参考文献

References

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.
Clarke, V., & Braun, V. (2017). Thematic analysis. Journal of Positive Psychology, 12(3), 297–298.

说明：

2006年论文是主题分析最核心的方法论来源
2019年论文是 Braun & Clarke 对"反思性主题分析"的重要修订，明确反对机械化六步执行
本 skill 的设计以反思性取向为指导，强调研究者主导判断，避免把主题分析变成流程执行

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.
Clarke, V., & Braun, V. (2017). Thematic analysis. Journal of Positive Psychology, 12(3), 297–298.

Notes:

The 2006 paper is the core methodological source for thematic analysis
The 2019 paper is an important revision by Braun & Clarke on "Reflexive Thematic Analysis", explicitly opposing mechanical six-step execution
The design of this skill is guided by the reflexive orientation, emphasizing researcher-led judgment and avoiding turning thematic analysis into process execution

语言

Language

默认中文
若用户用英文输入，输出用英文，frontmatter 字段名保持英文

Default language: Chinese
If the user inputs in English, output in English, frontmatter field names remain in English