ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Local Topic Mining for Reflective Medical Writing.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Utilizing natural language processing to analyze student narrative reflections for medical curriculum improvement.Medical teacher · 2025Article
- The Surgical Clerkship in the COVID Era: A Natural Language Processing and Thematic Analysis.The Journal of surgical research · 2024Article
- A systematic scoping review of reflective writing in medical education.BMC medical education · 2023Article
- TopEx: topic exploration of COVID-19 corpora - Results from the BioCreative VII Challenge Track 4.Database : the journal of biological databases and curation · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Reflective writing is used by medical educators to identify challenges and promote inter-professional skills. These non-medical skills are central to leadership and career development, and are clinically relevant and vital to a trainees success as a practicing physician. However, identification of actionable feedback from reflective writings can be chal- lenging. In this work, we utilize a Natural Language Processing pipeline that incorporates a seeded Term Frequency- Inverse Document Frequency matrix along with sentence-level summarization, sentiment analysis, and clustering to organize sentences into groups, which can aid educators in assessing common challenges experienced by Acting In- terns. Automated analysis of reflective writing is difficult due to its subjective nature; however, our method is able to identify known and new challenges such as issues accessing the electronic health system and adjusting to specialty differences. Medical educators can utilize these topics to identify areas needing attention in the medical curriculum and help students through this transitional time.
Identifiers
32477667PMC7233034W3030048597What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.