Adoption and Use of Mobile Learning in Continuing Professional Development by Health and Human Services Professionals
Bibliographic record
Abstract
INTRODUCTION: Health and human services professionals are increasingly using mobile devices to support clinical decision-making and evidence-based practice. However, research on self-directed learning in an era of growing digital technology utilization is underdeveloped. This study explored the adoption and use of mobile learning as a continuing professional development (CPD) activity. METHODS: A mixed-methods case study using semistructured interviews and a web-based questionnaire was conducted with health and human services professionals in Newfoundland and Labrador, Canada. RESULTS: Respondents reported using a smartphone (53.8%), tablets (50.4%), YouTube (43.0%), and mobile apps (35.8%) for CPD. The highest-rated benefits of mobile learning included improved access to information (M = 3.51); potential for enhanced knowledge acquisition (M = 3.45); staying up to date (M = 3.44); and verifying information (M = 3.40). The greatest barriers included cost of some apps and resources (M = 3.07); websites/programs not functional on mobile devices (M = 2.84); workplace barriers preventing access to digital resources (M = 2.82); and social media use linked to negative perceptions of professionalism (M = 2.65). Interview respondents described the flexibility and convenience of mobile learning, the level of autonomy it offered, and the advantages of learning on their own time. Technical issues, particularly for rural and remote practitioners, and digital professionalism also emerged as potential barriers. DISCUSSION: A systems model organizes the factors influencing the adoption and use of mobile devices and resources to support "just-in-time" learning. Addressing policies, practices, and regulations that enable or inhibit adoption of mobile learning for CPD may foster enhanced use to support better clinical decision-making, improved accuracy, and greater patient safety.
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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 | 0.007 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".