Patient and carer perceptions of case management for long-term conditions
Bibliographic record
Abstract
Nurse-led case management programmes have become increasingly popular over the last 15 years. Countries such as the USA, Canada, Sweden and the Netherlands have long running case management programmes in place for frail elderly people. The Department of Health in England has recently introduced a 'community matron' role to provide case management to patients with highly complex long-term conditions; a group that is predominantly comprised of elderly people. Department of Health policy documents do not define the day-to-day role of community matrons but instead describe the objectives and principles of case management for long-term conditions. The aim of this qualitative study was to describe case management from the perspective of patients and carers in order to develop a clearer understanding of how the model is being delivered for patients with long-term conditions. In-depth interviews were conducted with a purposive sample of 72 patients and 52 carers who had experience of case management. Five categories of case management tasks emerged from the data: clinical care, co-ordination of care, education, advocacy and psychosocial support. Psychosocial support was emphasised by both patients and carers, and was viewed as equally important to clinical care. Patient and carer perceptions of case management appear to contrast with descriptions contained in Department of Health guidance, suggesting an 'implementation surplus' in relation to the policy. This particularly appears to be the case for psychosocial support activities, which are not described in official policy documents. The provision of significant psychosocial support by community matrons also appears to differentiate the model from most other case management programmes for frail elderly people described in the literature. The findings emphasise the importance of seeking patient and carer input when designing new case management programmes.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".