A mixed-methods multi-site case study of a person-centred intervention for constant observation in hospitals with people living with dementia
Bibliographic record
Abstract
INTRODUCTION: Constant observation is widely used with people living with dementia admitted to hospital when identified at risk of harm to themselves or others. Staff allocated to closely monitor individual or small groups of patients intervene when there are safety concerns and may engage with patients' psychosocial needs. However, care is inconsistent and dependent upon individual and organisational factors. This study aimed to understand the work of implementing a co-designed intervention for facilitating person-centred approaches during constant observation practices. METHODS: A convergent parallel mixed-methods multi-site case study was adopted to explore implementation over 12 weeks in three English hospitals. The study recruited participants from six wards and one hospital-wide team. Qualitative and quantitative data involved: i) observations of staff-patient interactions (four time points), ii) in-depth interviews with hospital staff (one time point) iii) staff surveys (two time points). Qualitative data analysis was organised using Normalisation Process Theory to map and understand the implementation process. NOrmalisation MeAsure Development (NoMAD) survey data were analysed using descriptive statistics. FINDINGS: We recruited 163 participants - staff (n = 88), people living with dementia (n = 71), family supporters (n = 4). The intervention was well received and considered useful by staff. Incremental changes, such as staff initiating non-task related conversations with patients and using tools to inform actions for reducing distress, were observed. However, establishing the importance of psychosocial, alongside physical and medical, needs was not achieved. Staff found it difficult to challenge the dominance of medical management and organisations' priorities to minimise risk. Fears that discussions about constant observation with family supporters might upset them or result in accusations of inadequate care inhibited work to collect and share potentially useful information. CONCLUSION: The intervention endorsed and supported staff to focus on the quality of their care work; this was not usual practice. Routine use was impacted by prior knowledge of dementia, how the intervention aligned with ward practice and competing priorities. Additional work is required to support the shift from work organised as a reaction to urgent, risky situations to work that supports prevention and enhances care.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| 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".