Self-management toolkit and delivery strategy for end-of-life pain: the mixed-methods feasibility study
Bibliographic record
Abstract
BACKGROUND: Pain affects most people approaching the end of life and can be severe for some. Opioid analgesia is effective, but evidence is needed about how best to support patients in managing these medicines. OBJECTIVES: To develop a self-management support toolkit (SMST) and delivery strategy and to test the feasibility of evaluating this intervention in a future definitive trial. DESIGN: Phase I - evidence synthesis and qualitative interviews with patients and carers. Phase II - qualitative semistructured focus groups and interviews with patients, carers and specialist palliative care health professionals. Phase III - multicentre mixed-methods single-arm pre-post observational feasibility study. PARTICIPANTS: Phase I - six patients and carers. Phase II - 15 patients, four carers and 19 professionals. Phase III - 19 patients recruited to intervention that experienced pain, living at home and were treated with strong opioid analgesia. Process evaluation interviews with 13 patients, seven carers and 11 study nurses. INTERVENTION: Self-Management of Analgesia and Related Treatments at the end of life (SMART) intervention comprising a SMST and a four-step educational delivery approach by clinical nurse specialists in palliative care over 6 weeks. MAIN OUTCOME MEASURES: Recruitment rate, treatment fidelity, treatment acceptability, patient-reported outcomes (such as scores on the Brief Pain Inventory, Self-Efficacy for Managing Chronic Disease Scale, Edmonton Symptom Assessment Scale, EuroQol-5 Dimensions, Satisfaction with Information about Medicines Scale, and feasibility of collecting data on health-care resource use for economic evaluation). RESULTS: Phase I - key themes on supported self-management were identified from evidence synthesis and qualitative interviews. Phase II - the SMST was developed and refined. The delivery approach was nested within a nurse-patient consultation. Phase III - intervention was delivered to 17 (89%) patients, follow-up data at 6 weeks were available on 15 patients. Overall, the intervention was viewed as acceptable and valued. Descriptive analysis of patient-reported outcomes suggested that interference from pain and self-efficacy were likely to be candidates for primary outcomes in a future trial. No adverse events related to the intervention were reported. The health economic analysis suggested that SMART could be cost-effective. We identified key limitations and considerations for a future trial: improve recruitment through widening eligibility criteria, refine the SMST resources content, enhance fidelity of intervention delivery, secure research nurse support at recruiting sites, refine trial procedures (including withdrawal process and data collection frequency), and consider a cluster randomised design with nurse as cluster unit. LIMITATIONS: (1) The recruitment rate was lower than anticipated. (2) The content of the intervention was focused on strong opioids only. (3) The fidelity of intervention delivery was limited by the need for ongoing training and support. (4) Recruitment sites where clinical research nurse support was not secured had lower recruitment rates. (5) The process for recording withdrawal was not sufficiently detailed. (6) The number of follow-up visits was considered burdensome for some participants. (7) The feasibility trial did not have a control arm or assess randomisation processes. CONCLUSIONS: A future randomised controlled trial is feasible and acceptable. STUDY AND TRIAL REGISTRATION: This study is registered as PROSPERO CRD42014013572; Current Controlled Trials ISRCTN35327119; and National Institute for Health Research (NIHR) Portfolio registration 162114. FUNDING: The NIHR Health Technology Assessment programme.
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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".