Bibliographic record
Abstract
The rapid rise and widespread integration of digital technologies (e.g., smartphones, personal computers) into the fabric of our society has birthed a modern means of delivering healthcare, known as digital health. Through leveraging the accessibility and ubiquity of digital technologies, digital health represents an unprecedented level of reach, impact, and scalability for healthcare interventions, known as digital behaviour change interventions (DBCIs). The potential benefits associated with employing DBCIs are of particular interest for populations that are disadvantaged to receiving traditional healthcare, such as rural populations. However, several factors should be considered before implementing a DBCI into a rural environment, notably, digital health literacy. Digital health literacy describes the skills necessary to successful navigate and utilize a digital health solution (e.g., DBCI). Given their limited access to high-speed internet, higher cost associated for similar services, and poorer development of information and communication technologies (ICTs), most rural populations likely report lower digital health literacy – specifically, computer literacy, the ability to utilize and leverage digital technologies to solve problems. Hence, DBCIs should address this ‘digital divide’ between urban and rural populations before implementation. Practical solutions could include evaluating rural communities’ access to ICTs, needs assessments with rural community members, as well as integrating rural community stakeholders into the design of digital literacy education and interventions.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".