Reflections on the social epidemiologic dimension of health technology assessment
Bibliographic record
Abstract
Certain key parameters such as safety, efficacy, effectiveness, and cost effectiveness have long been established as key in HTA analysis. Equally important, however, are sociolegal and epidemiologic perspectives. A comprehensive analytic framework will consider the implications of using a technology in the context of societal norms, cultural values, and social institutions and relations. The methodology in which this expanded framework has been developed is termed 'Strategic HTA' to denote its power for the decision-making process. In addition to systematic reviews of published evidence, it incorporates analyses of the influence of dominant social relations on technological development and diffusion. This essay discusses the social epidemiologic aspects of health technology assessment, which includes factors such as sex and gender. It seeks to show how it is possible to bring data from wide-ranging disciplinary perspectives within the parameters of a single scientific inquiry; to draw from them scientifically defensible conclusions; and thereby to realize a deeper understanding of technology impact within a health care system. Armed with such an understanding, policy officials will be better prepared to resolve the competitive clamor of stakeholder voices, and to make the most "equitable" use of the available resources.
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 | 0.072 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".