Using the quadruple aim to understand the impact of virtual delivery of care within Ontario community health centres: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The onset of the COVID-19 pandemic and introduction of various restrictions resulted in drastic changes to 'traditional' primary healthcare service delivery modalities. AIM: To understand the impact of virtual care on healthcare system performance within the context of Ontario community health centres (CHCs). DESIGN & SETTING: Thematic analysis of qualitative interviews with 36 providers and 31 patients. METHOD: Semi-structured phone interviews were conducted in the autumn of 2020. Interviews were recorded, transcribed verbatim, and analysed thematically using the quadruple aim framework. RESULTS: The transition to virtual delivery of services has had both positive and negative impacts on care. Virtual care removed certain barriers to care such as access. However, patients and providers found that phone visits impacted quality of care owing to the inability to read body language and having to rely solely on patient descriptions. Video visits allowed for a similar experience to in-person visits but technical issues constrained this interaction. Depending on the service provided, some providers felt they were not providing the same quality of care. However, providers reported reductions in no-show rates and highlighted the efficiency of virtual appointments. Providers also found they were able to collaborate at a similar level before the pandemic and saw improvements in work-life balance. Overall, patients and providers alike preferred virtual visits with those known to them, and for less complex or transactional aspects of care. CONCLUSION: The study described positive and negative impacts on patient care, population health, health system costs, and provider experience. These results will be useful for primary care organisations in post-pandemic planning; however, future research is needed for a deeper exploration of the impact on quality of care specifically for more complex health concerns.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".