Combining web-based educational tools and mobile apps.
Bibliographic record
Abstract
315 Background: Mobile apps (MA) can be useful resources to provide point of care information. Over 50% of patients use a smartphone (SP), 75% of North American physicians own a SP, and 58% of SP users have downloaded mobile MA. MA are more readily accessible than web-based tools and usable without network connectivity. Methods: Cancer Care Ontario (CCO) has developed MA for patients and providers using the principles of end user satisfaction, simplicity and intuitiveness. These MA are taken from established web based, validated educational modules. Designed to ensure a rich native experience for each platform (WP7 and iOS), CCO’s process includes proof of concept, information design, and user interface design supported by user acceptance testing. Results: Two MA have been created: “CCO Drug Formulary” and “CCO Symptom Management Guidelines Application”. The first MA provides information on both drugs and regimens used in cancer treatment for patients and providers through alternate navigation in the same MA. From launch in October 2011 until June 2012, the Drug Formulary app had 4,280 downloads, including 39% from Asia and 14% from Europe, suggesting a global market. The second app provides symptom management guidelines for providers, hence supporting clinical decision making at point of care. From January 2011 to June 2012, the Symptom Management Guidelines MA had 4,050 downloads, in a mainly North American market. Both patient and provider feedback on preliminary evaluation has been positive. Conclusions: Patients and providers are receptive to MA as a tool to deliver real-time information at point of care. MA are another interface to existing technology solutions and other rich data sources. Further research into patient outcomes from adoption of such technology is needed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.028 |
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".