MSW Practice Research
What it means for a practitioner to write a paper
— A new approach to writing papers in collaboration with ChatGPT —
I have been thinking again recently about how papers should be written in social welfare studies.
In particular, the question of how to academicize the 26 years of experience I have accumulated as a medical social work practitioner.
In recent years, advanced quantitative research and sophisticated qualitative analysis have become the mainstream in academic papers.
Of course, that is important in itself and essential for the development of the discipline.
However, on the other hand, I also feel the following.
Wasn't social welfare originally a "discipline of practice"?
The successes, failures, hesitations, and conflicts that arise in practice.
Isn't it the accumulation and sharing of these that leads to the development of social work?
Therefore, I am exploring the direction ofpositioning practice reports as having the same value as general academic papersas a way forward.
1. Papers that start from practice
In many studies, the procedure followed is
"Formulate a question → Design the study → Collect data → Analyze".
However, for practitioners, it is a little different.
In the field, there is already a "prototype of a question".
Why did this support work well?
Why did this case not lead to the use of the system?
At what timing should the MSW have intervened?
In other words, hypotheses are dormant within practice.
I start bywriting down the goals that I can predict based on my gut feeling.
It has not been verified yet.
However, based on experience, I am almost certain.
At that stage, I write it down once.
This is my "hypothesis-first writing".
2. There is value whether it goes as expected or not
Proceed with research based on a hypothesis.
If the results are as expected, organize them into a paper as is.
What if you get unexpected results?
That in itself is extremely interesting.
When an expectation is missed, it means
your own assumptions and tacit knowledge have been visualized.
For a practitioner, the unexpected is not a "failure," but
the material with the highest academic value.
3. Collaborative style with ChatGPT
This is where AI comes in.
What I am conscious of when using ChatGPT is,
not having it write from scratch.
AI is not omnipotent.
In particular, the sense of the social welfare field will not emerge unless you provide the information.
I view AI as a tool that is better at "organizing" than "creating."
Therefore,
the paper's completion level can still be at 30%
but I think through 80-90% of the conceptual content myself
I collaborate with AI from this stage.
Specifically,
restructuring the organization
refining the verbalization
Correction of redundancies and ambiguous expressions
Pointing out leaps in logic
Assistance in connecting with prior research
I proceed with these tasks while engaging in dialogue with AI.
AI is not a ghostwriter, but a
sounding board to sharpen one's thinking.
4. Redefining the value of practice reports
I do not consider practice reports to be "inferior."
Rather, I see them as:
Cases that illuminate the gaps in the system
Cases of adult patients with congenital heart disease struggling during the transition period
The intersection of pension applications and employment support
I believe these individual cases are the raw gems that lead to institutional reform and policy recommendations.
Without the accumulation of practice reports, neither quantitative research nor policy research can stand.
In social welfare studies,
Practice → Verbalization → Sharing → Re-practice
This cycle is the essence.
5. Future policy
From now on, I will:
Submit practice reports as general academic papers
Establish a collaborative process with ChatGPT
Continue hypothesis-generating research through case studies
I will proceed with this policy.
Building a track record as a researcher is also important.
However, even more than that,
how much the support for those struggling in the field is articulated and shared
that is my standard.
Conclusion
Writing a paper is not about building achievements.
It is an act of handing over practice to the future.
And AI can be a tool to build that bridge.
Practitioners should not let their practice end as just practice.
They should put it into words, theorize it, and return it to society.
I want to continue exploring that process.
