Person-centred language for describing stratified approaches to TB treatment
Bibliographic record
Abstract
Person-centred language for describing stratified approaches to TB treatmentDear Editor, The prevailing 'one-size-fits-all' treatment paradigm for TB is changing with the recognition of the potential benefits of stratifying treatment based on the characteristics of the person with TB and disease presentation.[1][2][3][4] This shift is part of a larger movement to make TB care more effective, safe and person-centered.Another key aspect is ensuring the language used in TB care is purposeful, clear and non-stigmatizing.5,6 This raises the question of the most appropriate language to describe novel stratified treatment approaches to TB treatment.We therefore conducted a survey to understand the preferred terms used to describe stratified approaches to TB treatment among TB-affected communities, researchers, and providers.A short, anonymous online English language survey was conducted from May 10 to June 4, 2024.Informed by initial feedback from community partners reviewing a stratified medicine trial protocol, 10 pairs of terms were proposed.Respondents were asked to rate each as 'preferred', 'acceptable', or 'unacceptable' and provide open-ended reasons for their choice.We advertised our survey widely through directed e-mails to TB and HIV research networks' community advisory bodies, community organizations, and TB civil society listservs.The study was approved by The Johns Hopkins Medicine Institutional Review Board, Baltimore, MD, USA.In total, 108 individuals completed the survey.Respondents could select multiple identities, but most self-identified as members of civil society (66%) and/or people with lived experience with TB (43%).Healthcare providers (29%), researchers (23%) and TB program staff (21%) were represented.Respondents represented all WHO regions, with over half from the African region (57%), 19% from the South-East Asian Region, 16% from the region of the Americas (excluding the United States and Canada), 15% from the United States and Canada, 7% from Northern/Western/Southern Europe (including the United Kingdom), 3% from Eastern Europe, 2% from the Eastern Mediterranean region, and 1% from the Western Pacific region.All age groups (six categories ranging from 18 to 65þ) were represented, with the majority (31%) being between 35 and 44 years old.The most preferred pair of terms was 'shorter treatment and longer treatment,' with 46% of respondents indicating these terms as 'preferred' (Table ).These terms were the top preference across different demographic categories.The second and third most preferred terms also focused on the length of treatment:
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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 | 0.076 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".