Assimilation of Digital Health by Family Physicians in Canada
Bibliographic record
Abstract
Abstract Background: In recent years, governments, medical associations and other health care stakeholders have advocated digital health as a promising avenue to reduce inefficiencies, improve the delivery of health care services, increase the quality of primary care, and detect and manage infectious diseases. The present study aims to investigate the assimilation of digital health by family physicians, going beyond the simple adoption of these technologies to further understand the breadth and depth of their use in clinical practice for the diagnosis, treatment and prevention of disease, and for the monitoring of chronically-ill patients.Methods: The study was designed as an online survey. After conducting a pre-test of the questionnaire instrument, an invitation to participate in the study was sent to 7,664 members of the Quebec Federation of General Practitioners. The online questionnaire was developed on the Qualtrics survey platform. Data was collected from 768 family physicians, representing a 10% response rate.Results: Findings show that a large majority of the sampled physicians have yet to assimilate digital health within their medical practice. This is true in terms of both the physicians not incorporating digital health technologies into their routine work patterns and their not using these technologies to their full potential. The minority of respondents who showed a somewhat higher level of assimilation were motivated by patients’ greater requests related to digital health (e.g. for online consultations) and a greater chronic care caseload, and are characterized by their greater need for digital health training and their younger age, and use an EMR system that includes more functionalities (e.g. pharmaceutical advisers/prescribers).Conclusions: This study has policy implications in terms of the need for greater motivation and training of family physicians in order for them to make more extensive and innovative use of digital health technologies in their medical practice, for greater interoperability and integration of the digital health tools made available to them, and for greater awareness of their patients’ emerging attitudes and behaviors with regards to digital health. These implications make even more sense in the context of the current Covid-19 pandemic.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".