Canadian community pharmacists’ use of digital health technologies in practice
Bibliographic record
Abstract
BACKGROUND: In 2010, a pan-Canadian study on the current state and benefits of provincial drug information systems (DIS) found that substantial benefits were being realized and that pharmacists perceived DIS to be a valuable tool in the evolving models of pharmacy practice. To understand changes in digital health and the impact on practice since that time, a survey of community pharmacists in Canada was conducted. METHODS: In 2014, Canada Health Infoway (Infoway) and the Canadian Pharmacists Association (CPhA) invited community pharmacists to participate in a Web-based survey to understand their use and perceived benefits of digital health in practice. The survey was open from April 15 to May 12, 2014. RESULTS: Of the 447 survey responses, almost all used some form of digital health in practice. Those with access to DIS and provincial laboratory information systems (LIS) reported increased productivity and better quality of care. Those without access to these systems would overwhelmingly like access. DISCUSSION: There have been significant advances in digital health and community pharmacy practice over the past several years. In addition to digital health benefits in the areas of productivity and quality of care, pharmacists are also experiencing substantial benefits in areas related to recently expanded scope of practice activities such as ordering lab tests. CONCLUSION: Community pharmacists frequently use digital health in practice and recognize the benefits of these technologies. Digital health is, and will continue to be, a key enabler for practice transformation and improved quality of care. Can Pharm J (Ott) 2016;149:xx-xx.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".