Telehealth in Primary Healthcare: A Portrait of its Rapid Implementation during the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: This study documents the adoption of telehealth by various types of primary healthcare (PHC) providers working in teaching PHC clinics in Quebec during the COVID-19 pandemic. It also identifies the perceived advantages and disadvantages of telehealth. METHOD: A cross-sectional study was conducted between May and August 2020. The e-survey was completed by 48/50 teaching primary care clinics representing 603/1,357 (44%) PHC providers. RESULTS: Telephone use increased the most, becoming the principal virtual modality of consultation, during the pandemic. Video consultations increased, with variations by type of PHC provider: between 2% and 16% reported using it "sometimes." The main perceived advantages of telehealth were minimizing the patient's need to travel, improved efficiency and reduction in infection transmission risk. The main disadvantages were the lack of physical exam and difficulties connecting with some patients. CONCLUSION: The variation in telehealth adoption by type of PHC provider may inform strategies to maximize the potential of telehealth and help create guidelines for its use in more normal times.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".