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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».