Implementing digital respiratory technologies for people with respiratory conditions: A protocol for a scoping review
Bibliographic record
Abstract
The value of 'data-enabled', digital healthcare is evolving rapidly, as demonstrated in the COVID-19 pandemic, and its successful implementation remains complex and challenging. Harmonisation (within/between healthcare systems) of infrastructure and implementation strategies has the potential to promote safe, equitable and accessible digital healthcare, but guidance for implementation is lacking. Using respiratory technologies as an example, our scoping review process will capture and review the published research between 12th December 2013 to 12th December 2023. Following standard methodology (Arksey and O'Malley), we will search for studies published in ten databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Cochrane Library, Web of Science, Scopus, IEEE Xplore, CABI Global Health, and WHO Medicus. Our search strategy will use the terms: digital health, respiratory conditions, and implementation. Using Covidence, screening of abstracts and full texts will be undertaken by two independent reviewers, with conflicts resolved by a third reviewer. Data will be extracted into a pilot-tested data extraction table for charting, summarising and reporting the results. We will conduct stakeholder meetings throughout to discuss the themes emerging from implementation studies and support interpretation of findings in the light of their experience within their own networks and organisations. The findings will inform the future work within the ERS CONNECT clinical research collaboration and contribute to policy statements to promote a harmonised framework for digital transformation of respiratory healthcare.
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 | 0.139 | 0.135 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.077 | 0.018 |
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".