Exploring the use of digital technology to deliver healthcare services with explicit consideration of health inequalities in UK settings: A scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: To map and explore existing evidence on the use of digital technology to deliver healthcare services with explicit consideration of health inequalities in UK settings. Methods: We searched six bibliographic databases, and the National Health Service (NHS) websites of each UK nation (England, Scotland, Wales, Northern Ireland). Restrictions were applied on publication date (2013-2021) and publication language (English). Records were independently screened against eligibility criteria by pairs of reviewers from the team. Articles reporting relevant qualitative and/or quantitative research were included. Data were synthesised narratively. Results: Eleven articles, reporting data from nine interventions, were included. Articles reported findings from quantitative (n = 5), qualitative (n = 5), and mixed-methods (n = 1) studies. Study settings were mainly community based, with only one hospital based. Two interventions targeted service users, and seven interventions targeted healthcare providers. Two studies were explicitly and directly aimed at (and designed for) addressing health inequalities, with the remaining studies addressing them indirectly (e.g. study population can be classed as disadvantaged). Seven articles reported data on implementation outcomes (acceptability, appropriateness, and feasibility) and four articles reported data on effectiveness outcomes, with only one intervention demonstrating cost-effectiveness. Conclusions: It is not yet clear if digital health interventions/services in the UK work for those most at risk of health inequalities. The current evidence base is significantly underdeveloped, and research/intervention efforts have been largely driven by healthcare provider/system needs, rather than those of service users. Digital health interventions can help address health inequalities, but a range of barriers persist, alongside a potential for exacerbation of health inequalities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it