Virtual care post-pandemic: Why user engagement is critical to create and optimise future models of care
Bibliographic record
Abstract
Health systems are shifting from the use of virtual models of care reactively in response to the conditions of the pandemic, to deliberate planning for the integration of virtual models to enhance and extend current service provision. Use of virtual care in recent years has highlighted the critical role of clinician and consumer behaviour and mindsets in realising the opportunities of virtual care for improved health care and outcomes. Yet, the rapid and changing circumstances of the pandemic period provided limited opportunities for effective involvement of both clinicians and consumers in health system decision-making about when, how and which virtual services and associated technologies should be deployed. We explore the opportunity for enhanced engagement with these primary stakeholder groups to create quality healthcare as we emerge from the pandemic and enter a new phase of integrated virtual services.
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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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".