Experiences with using a mobile application for learning evidence-based practice in health and social care education: An interpretive descriptive study
Bibliographic record
Abstract
BACKGROUND: Health and social care students are expected to apply evidence-based practice (EBP). An innovative mobile application, EBPsteps, was developed to support learning EBP. AIM: The aim of this study was to explore health and social care students' experiences of learning about EBP using the mobile application EBPsteps during their clinical placements. METHODS: An interpretive description approach guided the exploration of student experiences. Four focus groups were conducted with a convenience sample of students from three undergraduate degree programs: occupational therapy, physical therapy, and social education. The constant comparison method was used to categorize and compare the qualitative data. RESULTS: Three integrated themes were generated: "triggers for EBP", "barriers to EBP", and "design matters". Information needs, academic requirements, and encouragement from clinical instructors triggered the students to use EBPsteps. Lack of EBP knowledge, lack of academic demand, and lack of emphasis on EBP in clinical placement were barriers to using EBPsteps. Design issues mattered, as use of the app was motivated by design features such as the opportunity to practice EBP in one place and taking notes in a digital notebook. The use of the app was hindered by anticipation that the use of phones during clinical placements would be viewed negatively by others and by specific design features, such as unfamiliar icons. CONCLUSIONS: The students perceived the EBPsteps app as a relevant tool for learning EBP, although they also suggested specific changes to the design of the app. Requirements must be embedded in the curriculum to ensure that the app is used. Our findings bring important information to developing and implementing mobile applications as a teaching method in health and social care educations.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".