Can Health-care Assistant Training improve the relational care of older people? (CHAT) A development and feasibility study of a complex intervention
Bibliographic record
Abstract
Background Older people account for an increasing proportion of those receiving NHS acute care. The quality of health care delivered to older people has come under increased scrutiny. Health-care assistants (HCAs) provide much of the direct care of older people in hospital. Patients’ experience of care tends to be based on the relational aspects of that care including dignity, empathy and emotional support. Objective(s) We aimed to understand the relational care training needs of HCAs caring for older people, design a relational care training intervention for HCAs and assess the feasibility of a cluster randomised controlled trial to test the new intervention against HCA training as usual (TAU). Design (1) A telephone survey of all NHS hospital trusts in England to assess current HCA training provision, (2) focus groups of older people and carers, (3) semistructured interviews with HCAs and other care staff to establish training needs and inform intervention development and (4) a feasibility cluster randomised controlled trial. Setting (1) All acute NHS hospital trusts in England, and (2–4) three acute NHS hospital trusts in England and the populations they serve. Participants (1) Representatives of 113 out of the total of 161 (70.2%) NHS trusts in England took part in the telephone survey, (2) 29 older people or carer participants in three focus groups, (3) 30 HCA and 24 ‘other staff’ interviewees and (4) 12 wards (four per trust), 112 HCAs, 92 patients during the prerandomisation period and 67 patients during the postrandomisation period. Interventions For the feasibility trial, a training intervention (Older People’s Shoes™) for HCAs developed as part of the study was compared with HCA TAU. Main outcome measures Patient-level outcomes were the experience of emotional care and quality of life during patients’ hospital stay, as measured by the Patient Evaluation of Emotional Care during Hospitalisation and the EuroQol-5 Dimensions questionnaires. HCA outcomes were empathy, as measured by the Toronto Empathy Questionnaire, and attitudes towards older people, as measured by the Age Group Evaluation and Description Inventory. Ward-level outcomes were the quality of HCA–patient interaction, as measured by the Quality of Interaction Scale. Results (1) One-third of trust telephone survey participants reported HCA training content that we considered to be ‘relational care’. Training for HCAs is variable across trusts and is focused on new recruits. The biggest challenge for HCA training is getting HCAs released from ward duties. (2) Older people and carers are aware of the pressures that ward staff are under but good relationships with care staff determine whether or not their experience of hospital is positive. (3) HCAs have training needs related to ‘difficult conversations’ with patients and relatives; they have particular preferences for learning styles that are not always reflected in available training. (4) In the feasibility trial, 187 of the 192 planned ward observation sessions were completed; the response to HCA questionnaires at baseline and at 8 and 12 weeks post randomisation was 64.2%, 46.4% and 35.7%, respectively, and 57.2% of eligible patients returned completed questionnaires. Limitations This was an intervention development and feasibility study so no conclusions can be drawn about the clinical effectiveness or cost-effectiveness of the intervention. Conclusions The intervention had high acceptability among nurse trainers and HCA learners. Viability of a definitive trial is conditional on overcoming specific methodological (patient recruitment processes) and contextual (involvement of wider ward team) challenges. Future work Methods to ease the burden of questionnaire completion without compromising ethics or methodological rigour need to be explored. Trial registration Current Controlled Trials ISRCTN10385799. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full inHealth Services and Delivery Research; Vol. 5, No. 10. See the NIHR Journals Library website for further project information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".