The Use of Person-Centered Language in Medical Research Journals Focusing on Psoriasis: Cross-sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Person-centered language places a person's identity before any disability or medical condition they may have. Using person-centered language reduces stigma and improves the patient-physician relationship, potentially optimizing health outcomes. Patients with psoriasis often feel stigmatized due to their chronic skin condition. OBJECTIVE: We seek to evaluate the use of person-centered language in psoriasis literature and to explore whether certain article characteristics were associated with non-person-centered language. METHODS: We performed a systematic search on PubMed for recently published articles in journals that regularly publish psoriasis studies. After article reduction procedures, randomization, and screening, we reached our target sample of 400 articles. The following non-person-centered language terms were extracted from each article: "Psoriasis Patient," "Psoriasis subject," "Affected with," "Sufferer," "Suffering from," "Burdened with," "Afflicted with," and "Problems with." Screening and data extraction occurred in a masked duplicate fashion. RESULTS: Of the 400 included articles, 272 (68%) were not adherent to person-centered language guidelines according to the American Medical Association Manual of Style. The most frequent non-person-centered language term was "Psoriasis Patient," found in 174 (43.5%) articles. The stigmatizing language was associated with the type of article and funding status, with original investigations and funded studies having higher rates of stigmatizing language. CONCLUSIONS: Articles about psoriasis commonly use non-person-centered language terms. It is important to shift away from using stigmatizing language about patients with psoriasis to avoid potential untoward influences. We recommend using "patients with psoriasis" or "patient living with psoriasis" to emphasize the importance of person-centered care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".