Patient experiences of UK primary care online triage and consultation platforms during COVID-19: A systematic review
Bibliographic record
Abstract
Abstract Background Online triage and consultation platforms are being increasingly used by healthcare providers in the UK for patient/clinician interactions. COVID-19 accelerated the shift towards the use of these platforms to maintain healthcare provision and reduce transmission. Strict directives were introduced by the UK Government to avoid in-person contact wherever possible in March 2020. Aim To examine patients’ experiences of online triage and consultation in UK primary care during the COVID-19 pandemic and offer considerations for their continued use. Design This study follows the PRISMA framework and includes qualitative studies conducted in UK primary care based on the experiences of patient users of any such online platform during the period of March 2020 to April 2023. Studies were included using the PICO format. Three literature databases were searched for relevant studies: PubMed, Science Direct and EMBASE. CASP is used to assess data quality. Results 540 studies were reviewed and reduced to 12 studies that met the inclusion criteria. Study characteristics were identified as: year of study, study population, disease types/conditions, patient response themes and the study’s data capture method. A thematic inductive approach identifies three overarching themes (Accessibility, Care delivery, System functionality) and 10 sub-themes (Affordability, IT literacy, Communication, Convenience, Care quality, Patient safety/privacy, Usability, Continuity of care, Inequality and Media influence). Conclusion This review highlights aspects of patient satisfaction and benefit but also those most concerning for patients. This study reviews the rapid, compulsory adoption of these systems during COVID-19 with implications for their future implementation beyond the pandemic.
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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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".