The virtual care-physicianism model: integrating foresight projections from hindsight and insight distilled data
Bibliographic record
Abstract
Purpose The purpose of this study is to examine Canadian physicians’ use of virtual health-care visits through three modalities – telephone, secure messaging and videoconferencing – before and after COVID-19. This study evaluates physicians’ perceptions of the usability of these modalities and measures the influence of six perceived behavioral factors on physician satisfaction, drawing on the proposed virtual care-physicianism model (VCPM) informed by Library and Information Science theory. Design/methodology/approach Using secondary data from the 2018 Canadian Physician Survey (n = 1,393) and the 2021 National Survey of Canadian Physicians (n = 2,071), this study addresses three research questions linked to corresponding hypotheses. Statistical analyses – including Z-tests, chi-square tests, multiple regression and hierarchical regression – were applied to assess changes in virtual care usage (Hindsight–Insight) and evaluate the conceptual, human and technical dimensions of the VCPM (Oversight). Findings The results of this study reveal a substantial increase in virtual care adoption, particularly videoconferencing (10.5% in 2018–50.4% in 2021). Usability scores in 2021 ranged from 2.68 to 4.23, with satisfaction differing significantly by age, gender and practice type. The VCPM explained 64% of the variance in satisfaction with virtual care. Key predictors included Perceived Work–Life Balance, Perceived Quality and Perceived Efficiency, underscoring the importance of integrating conceptual, human and technical factors to sustain adoption. Originality/value This study uniquely traces the evolution of Canadian virtual care within a pre-/post-pandemic framework, integrating Library and Information Science perspectives into health-care research. By combining national survey data with interdisciplinary theory, it offers a comprehensive resource for academics, policymakers and practitioners aiming to enhance physician satisfaction and sustain virtual care services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".