Strategic Management of Technology in Psychology: Implications for Decision-Making
Bibliographic record
Abstract
This review article systematically examines the significant advancements in epidemiological methods from 2005 to 2023, highlighting the evolution and impact of contemporary approaches in the field. Employing a thorough literature search across key databases, the review focuses on peer-reviewed articles, reviews, and meta-analyses that underscore innovative methodologies and applications in epidemiology. The inclusion criteria prioritized studies that introduced new techniques, integrated technology, or applied interdisciplinary approaches. This article synthesizes these advancements, revealing trends such as the incorporation of big data analytics, machine learning, and genetic epidemiology, which have substantially enhanced the scope and accuracy of epidemiological research. The review also discusses the challenges and ethical considerations emerging from these advanced methods, particularly in data privacy and the complexity of analysis. The findings underscore the shift towards more dynamic, precise, and interdisciplinary methods in epidemiology, reflecting the field's adaptation to the demands of modern public health challenges. This comprehensive overview not only provides a valuable resource for epidemiologists and public health professionals but also sets the stage for future research directions, emphasizing the need for continued innovation and ethical vigilance in epidemiological practices.
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 | 0.061 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".