Screening in Public Health and Clinical Care: Similarities and Differences in Definitions, Types, and Aims – A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: The concept of screening can be many times misleading to many people. This may be partly due to the way screening is described and explained in textbooks and journal articles. AIM: To review prominent public health and epidemiology textbooks, dictionaries, and relevant journal publications for definitions and examples of screening, with the aim of identifying common usages and concepts, as well as sources of potential confusion. MATERIALS AND METHODS: Commonly available epidemiology and public health textbooks and peer reviewed journals were searched for definitions and examples of screening. The search located seven journal articles, 10 textbooks, and one dictionary. The search platforms used were Pubmed, BIOSIS, EMBASE, Medline-OVID and Scopus under the Epidemiology and Biostatics subject head listed with Life Sciences. RESULTS: Descriptions of screening give varying emphasis to whether it is a test or a program, the aims of screening, the setting in which it is conducted, eligibility criteria, who initiates and who is intended to benefit and whether the condition being screened is an infectious or chronic disease or a risk-elevated state. Four essentially different 'types' of screening are described, using seven terms and occasionally contradictory examples. The detection of asymptomatic infectious cases is gradually changing from screening to surveillance as part of infection control. CONCLUSION: Voluntary screening programs rely on high participation to be effective and support and trust of the public are essential for the continued success of the public health profession. Consistent terminology is important for patients, providers and policymakers to understand what screening is and is not. Clear definitions are needed if we are to evaluate and communicate the risks and benefits of screening in public health.
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 | 0.027 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".