Safer and Effective Staffing Research and Policy Development Older People’s and Children’s Social Work in Northern Ireland:Report 1- The Starting Point: Baseline Analysis
Bibliographic record
Abstract
The issue of safe staffing in the Health and Social Care (HSC) sector has come to the fore because of recruitment and retention challenges, staff burnout, the impact of the COVID-19 pandemic, and exacerbated by the cost-of-living crisis, whereby those from areas of higher deprivation are at increased risk of statutory social work intervention (Bywaters et al., 2020; Limb, 2022; McFadden et al., 2015; McFadden et al., 2024a; McFadden et al., 2024b; Moriarty et al., 2018; Ravalier et al., 2022; The Guardian, Dec 2022; Vassilaki et al., 2022). The World Health Organisation (WHO) emphasises that safe staffing is not simply about the number of staff but also about having staff with required competencies equitably distributed and with support from the broader health system (WHO, 2016). Safe staffing should also mitigateburnout, workforce turnover and improve retention issues arising from workloads in excess of human capacity and highly stressed working environments (CIPD, 2022). In the UK, various operational tools and policy guidance govern staffing in different HSC sectors. Adult social care, regulated by the Care Quality Commission, defines safe staffing through specific guidelines (Care Quality Commission, 2024). Nurses adhere to policy guidance and tools such as the care hours per patient day (CHPD) to determine safe staffing levels (Carter, 2016; Gianassi and Rudman, 2018), the Shelford Safer Nursing Care Tool (2013), RCN Toolkit for Older People’s Wards (2012), Rhys Hearn (1970), the National Services Scotland Care Home Staffing Model (2009; as cited in Mitchell et al., 2017), and the Delivering Care Framework (2015) is similarly utilised in Northern Ireland. The Nursing and Midwifery Council underscores that safe staffing is not only about numbers but also skills-mix and considers other staff and settings (Nursing & Midwifery Council, 2016). There are less developed operational tools and frameworks established on safe staffing in social work. In the Department of Health, Northern Ireland, Social Work Workforce Review (2022, Recommendation 2b), safe staffing is a priority area, with regional consistency in social work practitioner numbers a current focus of attention (Davidson et al., 2022). In Scotland, regulations for safe staffing are outlined in the Integrated Health and Social Care Workforce Plan (2019) and legislation is due to be enacted in Scotland in 2024 (The Health and Care Staffing; Scotland Act 2019). Intensive research in Scotland on social worker caseloads is available in the ‘Setting the Bar’ Report (Millar & Barrie, 2022) published by Social Work Scotland. The report estimates indicative workloads for Childrens’ Services of no more than 15 cases (children) and for adults 20-25 cases per staff member. Experiences in the U.S. and Finland suggest that numbers alone may not guarantee a safe service (Child Welfare Information Gateway; Yliruka et al., 2022) however, numbers provide a baseline of what is realistic before social workers experience burnout and reduction in wellbeing.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".