Digital Microlearning for Training and Competency Development of Older Adult Care Personnel: Mixed Methods Intervention Study to Assess Needs, Effectiveness, and Areas of Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Older adult care organizations face challenges today due to high personnel turnover and pandemic-related obstacles in conducting training and competence development programs in a time-sensitive and fit-for-purpose manner. Digital microlearning is a method that attempts to meet these challenges by more quickly adapting to the educational needs of organizations and individual employees in terms of time, place, urgency, and retention capacity more than the traditional competency development methods. OBJECTIVE: This study aimed to determine if and how an app-based digital microlearning intervention can meet older adult care organizations' personnel competency development needs in terms of knowledge retention and work performance. METHODS: This study assessed the use of a digital microlearning app, which was at the testing stage in the design thinking model among managerial (n=4) and operational (n=22) employees within 3 older adult care organizations. The app was used to conduct predetermined competency development courses for the staff. Baseline measurements included participants' previous training and competency development methods and participation, as well as perceived needs in terms of time, design, and channel. They then were introduced to and used a digital microlearning app to conduct 2 courses on one or more digital devices, schedules, and locations of their own choice during a period of ~1 month. The digital app and course content, perceived knowledge retention, and work performance and satisfaction were individually assessed via survey upon completion. The survey was complemented with 4 semistructured focus group interviews, which allowed participants (in total 16 individuals: 6 managerial-administrative employees and 10 operational employees) to describe their experiences with the app and its potential usefulness within their organizations. RESULTS: The proposed advantages of the digital microlearning app were largely confirmed by the participants' perceptions, particularly regarding the ease of use and accessibility, and efficiency and timeliness of knowledge delivery. Assessments were more positive among younger or less experienced employees with more diverse backgrounds. Participants expressed a positive inclination toward using the app, and suggestions provided regarding its potential development and broader use suggested a positive view of digitalization in general. CONCLUSIONS: Our results show that app-based digital microlearning appears to be an appropriate new method for providing personnel competency development within the older adult care setting. Its implementation in a larger sample can potentially provide more detailed insights regarding its intended effects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it