How do medical and social contexts affect telemedicine efficiency and quality? A propensity-score matching protocol in Canada’s primary care
Bibliographic record
Abstract
INTRODUCTION: Telemedicine use has risen significantly since the COVID-19 pandemic. Evidence suggests that the quality of care in telemedicine could be as good as in-person care, but this is likely context-dependent. Expert guidelines have declared the appropriate medical conditions, but often without empirical evidence that grapples with the fundamental information limitations facing telemedicine. We draw on the task-technology fit theory and empirical evidence around human communication to examine how the medical and social contexts affect the efficiency and clinical quality of primary care. METHODS AND ANALYSIS: We will use a population-based dataset from the Canadian province of British Columbia (BC) to inform a quasi-experimental study using propensity score matching (PSM). The treatment group will consist of telemedicine visits from April to December 2022. We will use PSM to create a control group of matched, in-person visits in the same period. We will then use cluster-robust linear regression to identify how specific medical conditions and social contexts are associated with higher rates of prescription, follow-up with primary care providers, emergency department visits and acute care admissions. We plan for the study to take place from 1 August 2025 to 1 August 2026. ETHICS AND DISSEMINATION: The Research Ethics BC has granted approval for this study (H21-02244-A006). Our findings will be shared with patients, healthcare providers and policymakers and disseminated through conference presentations and peer-reviewed publications.
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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 | 0.073 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".