Technology Assessment and the Sociopolitics of Health Technologies
Bibliographic record
Abstract
In a growing number of countries, health technology assessment (HTA) has come to be seen as a vital component in policy making. Even though the assessment of the social, political, and ethical aspects of health technology is listed as one of its main objectives, in practice, the integration of such dimensions into HTA remains limited. Recent social scientific research on the inherently political nature of technology strongly supports such a comprehensive approach. The growing claims by and on behalf of consumer groups also suggest that HTA should be informed by a broader set of perspectives. Using the example of the cochlear implant in children, this essay compares the professed objectives of HTA with typical practice and explores possible explanations for the discrepancies observed. A second example, home telemonitoring for elderly persons, demonstrates how the types of evidence considered by HTA and the process through which assessments are produced may be reconsidered. We argue for the formal integration of the sociopolitical dimensions of health care technologies into assessments. The ability of HTA to more fully address important issues from a public policy point of view will increase by making explicit the sociopolitical nature of health care technologies.
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 | 0.039 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| 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".