The Digital Skills, Experiences and Attitudes of the Northern Ireland Social Care Workforce Toward Technology for Learning and Development: Survey Study
Bibliographic record
Abstract
Background Continual development of the social care workforce is a key element in improving outcomes for the users of social care services. As the delivery of social care services continues to benefit from innovation in assistive technologies, it is important that the digital capabilities of the social care workforce are aligned. Policy makers have highlighted the importance of using technology to support workforce learning and development, and the need to ensure that the workforce has the necessary digital skills to fully benefit from such provisions. Objective This study aims to identify the digital capability of the social care workforce in Northern Ireland and to explore the workforce’s appetite for and barriers to using technology for learning and development. This study is designed to answer the following research questions: (1) What is the digital capability of the social care workforce in Northern Ireland? (2) What is the workforce’s appetite to participate in digital learning and development? and (3) If there are barriers to the uptake of technology for learning and development, what are these barriers? Methods A survey was created and distributed to the Northern Ireland social care workforce. This survey collected data on 127 metrics that described demographics, basic digital skills, technology confidence and access, factors that influence learning and development, experience with digital learning solutions, and perceived value and challenges of using technology for learning. Results The survey was opened from December 13, 2018, to January 18, 2019. A total of 775 survey respondents completed the survey. The results indicated a workforce with a high level of self-reported basic digital skills and confidence. Face-to-face delivery of learning is still the most common method of accessing learning, which was used by 83.7% (649/775) of the respondents; however, this is closely followed by digital learning, which was used by 79.0% (612/775) of the respondents. There was a negative correlation between age and digital skills (rs=−0.262; P<.001), and a positive correlation between technology confidence and digital skills (rs=0.482; P<.001). There was also a negative correlation between age and the perceived value of technology (rs=−0.088; P=.02). The results indicated a predominantly motivated workforce in which a sizable portion is already engaged in informal digital learning. The results indicated that lower self-reported basic digital skills and confidence were associated with less interest in engaging with e-learning tools and that a portion of the workforce would benefit from additional basic digital skills training. Conclusions These promising results provide a positive outlook for the potential of digital learning and development within the social care workforce. The findings provide clear areas of focus for the future use of technology for learning and development of the social care workforce and considerations to maximize engagement with such approaches.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".