A144: Resident's Guide to Rheumatology Guide Mobile Application: An International Needs Assessment
Bibliographic record
Abstract
Background/Purpose: “A Resident's Guide To Pediatric Rheumatology” (the Guide) is a widely accepted resource for pediatric rheumatologists and trainees. In preliminary assessments, uptake of the Guide was broader than intended and it was used by trainees to help with clinical decision‐making, learning and teaching. Users of the Guide suggested that it be developed into a mobile application (app) The Technology Acceptance Model (TAM) provides a framework to assess the perceived usefulness and ease of use of a tool to predict future acceptance and use. Objectives: To determine the International demand amongst pediatric professionals and current trainees for a mobile app format of the Guide. To determine user preferred features, functions, and format to be included in a mobile app using the TAM. Methods: An electronic survey was developed and distributed to pediatric residents at SickKids hospital and to both faculty and trainee members of the international Pediatric Rheumatology list server. The survey included respondent demographics, perceived usefulness, perceived ease of use, and behavioural intention to use the app based on the TAM. Data were analyzed using descriptive statistics. Results: The survey was distributed to 75 pediatric residents and 1132 members of the Pediatric Rheumatology listserver and 135 (12% response rate) completed the survey. The majority of respondents were rheumatologists (53%), while the remainder consisted of Fellows (17%), Pediatric Residents (16%), and other allied health professionals (5%). 93% owned a smartphone and 58% owned a tablet. Most had medically related apps (75%) compared to e‐books (38%), but had similar use for each—Mone to several times per week for 1–15 minutes each time on average. The most useful features of an app would be clinical pictures (e.g. skin rashes), radiology images (e.g. joint x‐rays), and definitions of key terms. Least useful features were games and multiple‐choice questions. Additional features included a searchable index and links to journal articles. Looking at the TAM, the vast majority of respondents thought that the mobile app would enhance trainees' learning and teaching effectiveness. Greater than 80% of respondents consistently supported its perceived ease of use. 55% stated that they were likely to use the app often. 86% felt it was important for the app to be developed. If the app was not available for free, a majority (43%) of respondents were willing to pay for the app with a most willing to pay up to $5.00, and 10% willing to pay up to $10 for access to the app. Conclusion: Development of the Guide app was well supported with adding features such as clinical photographs, radiology images, definitions and searchable index. TAM showed the intention to use the app in the future will be most determined by the perceived ease of use which was consistently high in the survey. Interestingly, users were willing to pay for the app if it was not free. Future steps include a qualitative study ultilizing focus groups to assess the perceived functionality, usability, facilitators and barriers in using the Guide app prototype to create the most targeted, user friendly app.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".