Self-reported use of handheld computers: a survey of Nova Scotia pharmacists
Bibliographic record
Abstract
Introduction: With an ever-increasing array of pharmaceutical and biomedical products and literature, health professionals including pharmacists struggle to obtain, evaluate, and apply relevant information. Handheld computers provide pharmacists with mobile access to evidence-informed medical information, decision support tools, and the ability to monitor therapeutic outcomes at the point of care. There is limited literature on the usage of this technology by Canadian pharmacists. The objective of this survey was to determine the scope and nature of handheld computer use by Nova Scotia pharmacists. Method: In 2008, Nova Scotia pharmacists were contacted with a written survey. Descriptive statistics were used to compare users and non-users. Multivariate regression analysis was used to determine demographic and pharmacy practice variables that might be associated with pharmacists’ use of handheld computers. Results: The survey was returned by 296 pharmacists (27.7%). Handheld computers were reported to be used by 51% of respondents. Those respondents who have been in practice longer were less likely to adopt handheld computer use (adjusted OR = 0.97, 95% CI = 0.94–0.99, p = 0.01). Barriers and facilitators to usage were explored. More than two-thirds of pharmacists who had not yet used handheld computers perceived a future value for these devices within their practice. Discussion: Pharmacists are adopting the use of handheld computers. With enhanced clinical practice opportunities for pharmacists including independent prescribing, these tools may offer needed functionality. Further work is required to understand the value of handheld computers as information resources, which may improve the effectiveness and efficiency of patient care.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".