An Exploration of Practitioners’ Experiences of Delivering Digital Social Care Interventions to Children and Families During the COVID-19 Pandemic: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Digital technology is an increasing feature of social care practice, and its use has accelerated greatly in response to the COVID-19 pandemic. OBJECTIVE: This study aimed to assess social care practitioners' experiences of delivering digital interventions to vulnerable children and families during the pandemic. METHODS: A mixed methods study combining survey and qualitative research was conducted. In total, 102 social care practitioners working in the Republic of Ireland who delivered a range of digital social care support took part in a web-based survey. This survey captured practitioners' engagement and experiences of delivering digital social care interventions to children and families as well as training and capacity building needs. Subsequently, 19 focus groups with 106 social care practitioners working with children and families were also conducted. These focus groups were directed by a topic guide and explored in more depth practitioners' perceptions of digital social care practice, the perceived impact of digital technology on their work with children and families, and the future application of digital social care interventions. RESULTS: The survey findings revealed that 52.9% (54/102) and 45.1% (46/102) of practitioners, respectively, felt "confident" and "comfortable" engaging in digital service delivery. The vast majority of practitioners (93/102, 91.2%) identified maintaining connection during the pandemic as a benefit of digital social care practice; approximately three-quarters of practitioners (74/102, 72.5%) felt that digital social care practice offered service users "increased access and flexibility"; however, a similar proportion of practitioners (70/102, 68.6%) identified inadequate home environments (eg, lack of privacy) during service provision as a barrier to digital social care practice. More than half of the practitioners (54/102, 52.9%) identified poor Wi-Fi or device access as a challenge to child and family engagement with digital social care. In total, 68.6% (70/102) of practitioners felt that they needed further training on the use of digital platforms for service delivery. Thematic analysis of qualitative (focus group) data revealed 3 overarching themes: perceived advantages and disadvantages for service users, practitioners' challenges in working with children and families through digital technologies, and practitioners' personal challenges and training needs. CONCLUSIONS: These findings shed light on practitioners' experiences of delivering digital child and family social care services during the COVID-19 pandemic. Both benefits and challenges within the delivery of digital social care support as well as conflicting findings across the experiences of practitioners were identified. The implications of these findings for the development of therapeutic practitioner-service user relationships through digital practice as well as confidentiality and safeguarding are discussed. Training and support needs for the future implementation of digital social care interventions are also outlined.
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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 | 0.040 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".