VP110 Building Capacity In Health Technology Assessment Through Plain Language
Bibliographic record
Abstract
INTRODUCTION: Health Technology Assessments (HTAs) and policy papers are generally written in academic style using industry jargon — pharmaceutical, medical, or scientific terminology — with a generous use of abbreviations. Transforming technical or biomedical data into easily understandable text is a necessity and a challenge for all of us if our goal is to facilitate HTA collaboration beyond borders and integrate HTA into healthcare practice. Many countries have legislated for plain language (PL), and organizations globally are beginning to recognize how it helps in the uptake of information, whether geared to healthcare professionals and all those interested in HTA, or the lay public. METHODS: A preliminary, informal online search for legislative and supporting guidance on PL was conducted, and a query sent out to forty-eight International Network of Agencies for Health Technology Assessment (INAHTA) members. RESULTS: • The United States Plain Writing Act of 2010 has legislated that federal agencies use “clear Government communication that the public can understand and use” (1). Of the twenty-one respondents from INAHTA Listserv, seven use plain language in either their knowledge transfer tools (such as executive and research summaries, booklets and fact sheets, and patient or lay material). • The Government of Canada promotes plain language in all of its communications (2). • McMaster University's 2014 Health Forum on strengthening public and patient engagement in HTA in Ontario supported “clarity and consistency in the use of public- and patient-engagement terminology” in HTAs. • A growing number of international health-related and HTA organizations promote PL in their reports and HTAs to help with their health literacy. • Many pharmaceutical companies encourage PL communication in their writing (3). • Of the eighteen INAHTA responses received, eight reported that they use PL in their report summaries, knowledge transfer materials, and/or patient education tools. CONCLUSIONS: Adopting the practice of clear, straightforward writing and editing in all biomedical communication — including HTAs and journal articles — encourages interaction and engagement among patient, public, and healthcare stakeholders invested in HTAs, and their desire to have measured decision making based on comprehensive, informed, and easily understandable information. However, it remains to be seen if PL will be embraced by organizations worldwide. This preliminary, informal inquiry as to its use suggests that the adoption of PL by governments, HTA organizations, and the scientific community worldwide has not yet been fully embraced.
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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 | 0.095 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.018 |
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".