Personal digital assistant usage among undergraduate medical students: exploring trends, barriers, and the advent of smartphones
Bibliographic record
Abstract
Personal digital assistant (PDA) use is widespread and now considered an essential part of medical practice [1,2].These devices, also known as handhelds, allow health professionals to find answers to clinical questions while at the patient's bedside, resulting in better patient safety and clinical outcomes [3].However, it has been found that PDA users, especially medical students, often struggle to use these tools to their full potential [4][5][6][7].Health sciences libraries have responded by providing resources and services such as the evaluation of PDA programs and devices, instruction sessions on effective PDA use, and even the provision of technical support [8,9].Most of the literature that can be used to inform library practices in this area only briefly describes PDA usage patterns [10,11], the types of activities performed with a PDA, or the most popular programs used [2,5,7,[12][13][14][15][16][17].Some studies go slightly further and examine barriers to handheld computing [6,7,14,18].While a multitude of case studies superficially describe PDA initiatives implemented by libraries [19][20][21][22], few studies empirically examine the services and resources provided for medical students who use PDAs [23].Finally, little to nothing has been written on how the advent of the smartphone has affected PDA usage and users' behaviours and needs [24].Since 2001, the John W. Scott Health Sciences Library at the University of Alberta (U of A) has been providing services and resources for those using PDAs.These services have met with some success and have been cited and commended by a number of authors [19,25,26].In an area where so much innovation and change takes place, it is necessary for libraries to maintain relevance by routinely assessing services and implementing improvements.This paper describes a study that was developed to inform the U of A's current PDA initiatives and subsequently addresses some of the gaps identified in the current body of literature on this topic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".