Integration of ethical considerations into HTA reports: an analysis of integration levels using a systematic review
Bibliographic record
Abstract
OBJECTIVE: To describe the type and level of ethical integration in published health technology assessment (HTA) reports and systematically identify the ethical approaches utilized. METHODS: A literature search was conducted with the Google™ search engine using the keyword "ethic" between 1 January 2015 and 20 August 2019. Only HTA assessment reports with a section on ethics were retained and classified according to their level of ethical integration: no ethical analysis, ethical issues highlighted, assessments according to legal or social norms, and assessments from a moral or axiological perspective-using a qualitative methodology to distinguish such integration. RESULTS: This review yielded 188 reports with a section identified as being on ethics, produced by seventeen HTA agencies in eleven countries. One hundred and thirty-six reports did not develop an ethical analysis, thirty-one highlighted ethical issues, seventeen conducted a norm-based ethical assessment using a descriptive approach grounded in social norms, and four developed an assessment grounded in a moral or axiological perspective. The bioethical "four-principles" framework was used, but mainly for presenting ethical issues and not as a moral framework. CONCLUSIONS: The majority of reports featuring a section on ethics mention ethical considerations without ethical analysis. Ethical issues are grouped with legal, social, and organizational issues and treated as contextual considerations that decision makers should be aware of. When reports present systematic norm-based ethical assessments from a descriptive perspective or ethical assessment based on a moral or axiological perspective, there is a tendency to ground these analyses in frameworks created for the purpose and reliant on a concept of ethics supporting them.
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 | 0.252 | 0.604 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.093 | 0.084 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".