A qualitative study on the use of personal digital assistants and smartphones among interns and residents of Hazrat Rasool Akram and Imam-Khomeini hospitals
Bibliographic record
Abstract
Introduction \nHandheld computers such as Personal Digital Assistants (PDAs) and smartphones are playing an increasing role to improve healthcare services by making information available at the point of care. The aim of the present study was to investigate how medical interns and residents retrieve information when using handheld devices. Furthermore, This study investigates the benefits and barriers of using handheld computers in clinical activities, and factors affecting adoption handheld computers among medical interns and residents. \nMethodology \nWe conducted this qualitative study, using a semi-structured interview protocol. We selected the subjects of the study, using the purposive snowball sampling method. We carried out in-depth interviews with 21 medical interns and residents from May through September 2011. NVivo Software was use to codify and analyze the data. \nFindings \nParticipants usually used PDAs and smartphones for web search, personal information management and communication in daily activities. They also used handheld computers for searching medical databases and drug references, reading electronic books and web search. Participants believed that usefulness, ease of use, education, organizational factors, individual features, social influences, observability, compatibility and job characteristics were the main factors affecting the adoption of these devices in medical settings. It was found that the most important benefits of using handheld computers included easy access and instant delivery of health information, medical errors reduction, access to evidence based and up-to-date medical information, portability, improving clinical decision makings and treatment quality. Three limitation categories of usage were also identified upon data analysis: Limitations related to handheld computers, individual barriers, and external limitations. \n \nConclusion \nSignificant development and effective changes are taking place in the field of medicine by the emergence of handheld computers in clinical settings; but in developing countries like Iran it is in its childhood period and needs more attention. It is important to use supportive tools like mobile technologies in healthcare organizations for better patient care. Healthcare managers and policy makers can use findings of current study for a better integration of handheld computers in medical settings. They can apply encouraging factors in hospitals, medical centers and clinics to make mobile technologies as popular devices among healthcare professionals. \n
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".