“My UC Story”: A Qualitative Descriptive Study Describing the Patient Journey for Ulcerative Colitis
Bibliographic record
Abstract
Background: Personal perspectives of patients are seldomly reported in the literature, most notably their journey to diagnosis. Literature is heavily focused on the patient journey from a healthcare professional’s point of view during the treatment process. The objective of this study is to conduct a qualitative study on a video sharing site, YouTube, to determine if the patient journey from a subjective perspective is truly linear for those who suffer from ulcerative colitis. Methods: Phrases searched on YouTube included “ulcerative colitis story” and “ulcerative colitis diagnosis story”. Video monologues chronicling the patient journey before diagnoses were transcribed using the YouTube transcription function to identify patterns amongst users’ experiences. Thematic analysis was used to identify whether certain themes were present in the monologues. Analysis was performed using NVivo 12 QRS International and used line-by-line coding to create an initial codebook that represented the concepts covered in the monologues. Results: We viewed a total of 48 videos and included 29 videos from 2010 to 2020 for qualitative analysis. Overall, three major themes were identified in the patient journey prior to ulcerative colitis diagnosis:1) initial symptoms 2) initial encounter with the healthcare system 3) gastroenterologist referral. Conclusions: The literature depicts the patient journey as a linear path. This qualitative study discovers that the reality of the patient journey is in fact non-linear. Pharmacists are the most accessible health care professionals and can help guide patients in prioritizing signs and symptoms to streamline the non-linear path that patients experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".