Ethical issues in public health surveillance: a systematic qualitative review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Public health surveillance is not ethically neutral and yet, ethics guidance and training for surveillance programmes is sparse. Development of ethics guidance should be based on comprehensive and transparently derived overviews of ethical issues and arguments. However, existing overviews on surveillance ethics are limited in scope and in how transparently they derived their results. Our objective was accordingly to provide an overview of ethical issues in public health surveillance; in addition, to list the arguments put forward with regards to arguably the most contested issue in surveillance, that is whether to obtain informed consent. METHODS: Ethical issues were defined based on principlism. We assumed an ethical issue to arise in surveillance when a relevant normative principle is not adequately considered or two principles come into conflict. We searched Pubmed and Google Books for relevant publications. We analysed and synthesized the data using qualitative content analysis. RESULTS: Our search strategy retrieved 525 references of which 83 were included in the analysis. We identified 86 distinct ethical issues arising in the different phases of the surveillance life-cycle. We further identified 20 distinct conditions that make it more or less justifiable to forego informed consent procedures. CONCLUSIONS: This is the first systematic qualitative review of ethical issues in public health surveillance resulting in a comprehensive ethics matrix that can inform guidelines, reports, strategy papers, and educational material and raise awareness among practitioners.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it