Defining the Scope of Digital Public Health and Its Implications for Policy, Practice, and Research: Protocol for a Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND There has been rapid development and application of digital technologies in public health domains, which are considered to have the potential to transform public health. However, this growing interest in digital technologies in public health has not been accompanied by a clarity of scope to guide policy, practice, and research in this rapidly emergent field. OBJECTIVE This scoping review seeks to determine the scope of digital health as described by public health researchers and practitioners and to consolidate a conceptual framework of digital public health. METHODS The review follows Arksey and O’Malley’s framework for conducting scoping reviews with improvements as suggested by Levac et al. The search strategy will be applied to Embase, Medline, and Google Scholar. A grey literature search will be conducted on intergovernmental agency websites and country-specific websites. Titles and abstracts will be reviewed by independent reviewers, while full-text reviews will be conducted by 2 reviewers to determine eligibility based on prespecified inclusion and exclusion criteria. The data will be coded in an iterative approach using the best-fit framework analysis methodology. RESULTS This research project received funding from the British Columbia Centre for Disease Control Foundation for Population and Public Health on January 1, 2020. The initial search was conducted on June 1, 2020 and returned 6953 articles in total. After deduplication, 4523 abstracts were reviewed, and 227 articles have been included in the review. Ethical approval is not required for this review as it uses publicly available data. CONCLUSIONS We anticipate that the findings of the scoping review will contribute relevant evidence to health policy makers and public health practitioners involved in planning, funding, and delivering health services that leverage digital technologies. Results of the review will be strategically disseminated through publications in scientific journals, conferences, and engagement with relevant stakeholders. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/27686
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 | 0.179 | 0.216 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.098 | 0.028 |
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".