Feasibility of an electronic survey on iPads with in-person data collectors for data collection with health care professionals and health care consumers in general emergency departments
Bibliographic record
Abstract
Background: Translating Emergency Knowledge for Kids was established to bridge the research-practice gap in pediatric emergency care by bringing the best evidence to Canadian general emergency departments (EDs). The first step in this process was to conduct a national needs assessment to determine the information needs and preferences of health professionals and parents in this clinical setting. Objective: To describe the development and implementation of two electronic surveys, and determine the feasibility of collecting electronic survey data on iPads with in-person data collectors in a busy clinical environment. Methods: Two descriptive surveys were conducted in 32 general EDs. Specific factors were addressed in four survey development and implementation stages: survey design, survey delivery, survey completion, and survey return. Feasibility of the data collection approach was determined by evaluating participation rates, completion rates, average survey time to completion, and usability of the platform. Usability was assessed with the in-person data collectors on five key variables: interactivity, portability, innovativeness, security, and proficiency. Results: Health professional participation rates (1561/2575, 60.62%) and completion rates (1471/1561, 94.23%) were strong. Parental participation rates (974/1099, 88.63%) and completion rates (897/974, 92.09%) were excellent. Mean time to survey completion was 28.08 minutes for health professionals and 43.23 minutes for parents. Data collectors rated the platform “positively” to “very positively” on all five usability variables. Conclusions: A number of design and implementation considerations were explored and integrated into this mixed-mode survey data collection approach. Feasibility was demonstrated by the robust survey participation and completion rates, reasonable survey completion times, and very positive usability evaluation results.
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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 | 0.117 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".