Mixed methods data collection using ipads: experiences from the Translating Emergency Knowledge for Kids (TREKK) project
Bibliographic record
Abstract
In Canada, the majority of children requiring emergency care are treated in general emergency departments (EDs). Evidence shows that up to 40% of children treated in general EDs do not receive treatments for which clear evidence exisits and up to 20% of these children receive a treatment that has been shown to provide no benefit or even causes harm. The Translating Emergency Knowledge for Kids (TREKK) project is aimed at ensuring the latest research in pediatric emergency medicine is applied in general EDs. In the first phase of TREKK, we have partnered with 35 general EDs across Canada to determine knowledge needs and preferences of ED healthcare providers and families seeking care. In this mixed methods study, healthcare professionals and parents seeking care for their children will complete electronic surveys via a custom iPad ‘app.’ SPSS will be used to analyze questionnaire data. Sites will also be purposively sampled to participate in qualitative data collection. The camera, video, notes, and voice memo iPad functions will be used to document the general ED experiences of both populations and serve as prompts during individual interviews, which will be analyzed thematically. The creation of the data collection tools, the electronic platform, and attending to research ethics boards/operational approval boards has been a complex and labour intensive processes. We believe novel technology increases participant engagement and enhances large scale data collection; however, in our experience, it was necessary to rethink traditional approaches to research coordination and administration. We intend to share ‘lessons learned’ from TREKK.
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 | 0.098 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".