Applications of information and communications technologies to public health: A scoping review using the MeSH term “public health informatics”
Bibliographic record
Abstract
OBJECTIVE: To investigate the public health domains, key informatics concepts, and information and communications technologies (ICTs) applied in articles that are tagged with the MeSH term "public health informatics" and primarily focus on applying ICTs to public health. MATERIALS AND METHODS: The MeSH term "public health informatics" was searched on MEDLINE-PubMed. The results of the search were then screened in two steps in order to only include articles about applying ICTs to public health problems. First, articles were screened based on their titles and abstracts. Second, a full-text review was conducted to ensure the relevance of the included articles. All articles were charted based on public health domain, information technology, article type, and informatics concept. RESULTS: 515 articles were included. Communicable disease monitoring (N=235), public health policy and research (N=201), and public health awareness (N=85) constituted the majority of the articles. Inconsistent results were found regarding the validity of syndromic surveillance and the effectiveness of PHI integration within the healthcare systems. DISCUSSION: PHI articles with an ICT focus cover a wide range of themes. Collectively, the included articles emphasized the need for further research in interoperability, data quality, appropriate data sources, accessible health information, and communication. The limitations of the study include:1) only one database was searched; 2) by using MeSH tags as a selection criterion, PHI articles without the "public health informatics" MeSH term were excluded. CONCLUSION: Due to the multi-disciplinary nature of PHI, MeSH identifiers were not assigned consistently. Current MeSH-tagged articles indicate that a comprehensive approach is required to integrate PHI into the healthcare system.
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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.037 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".