Mapping care trajectories for hospital-initiated benzodiazepine deprescribing in older adults: a multicentre qualitative study
Bibliographic record
Abstract
BACKGROUND: Long-term prescription of benzodiazepine receptor agonists (BZRAs) for insomnia in adults over 65 years of age is considered a low-value care practice. Although deprescribing has emerged as a possible solution, the implementation of BZRA deprescribing has been considered insufficient in routine clinical care. Mapping care trajectories relative to BZRA deprescribing can help identify inefficiencies in current care and propose strategies to enhance the implementation of deprescribing interventions in the long term. This study aims to map care trajectories and characterise contextual factors relevant to hospital-initiated BZRA deprescribing in older adults. METHODS: A qualitative study was conducted with local researchers, healthcare professionals (HCPs), patients, and informal carers in six European countries (Belgium, Greece, Norway, Poland, Spain, and Switzerland). Three theoretical frameworks were used to guide data collection and analyses: 1) the ‘6W’ multidimensional model of care trajectories; 2) the patient-centred deprescribing process; and 3) the Context and Capabilities for Integrating Care framework. Data was collected online through self-administered questionnaires, individual interviews, and group validation interviews. Data were transcribed verbatim and analysed using a combination of deductive and inductive thematic analyses. RESULTS: We collected 18 responses to surveys, and conducted 53 interviews (6 local researchers, 36 HCPs, 11 patients or informal carers) and 7 group interviews (comprising 28 HCPs, 4 patients). We developed nine validated care trajectory maps by hospital department and type of unit, and two general comparative maps for inpatient and outpatient care. Hospital-initiated BZRA deprescribing emerged as a complex healthcare process that is likely to involve several HCPs from hospital and primary care. Participants considered patient involvement in shared decision-making (SDM) to be crucial in deprescribing, but implementation was scarce in routine care. The type of HCPs involved and the communication channels between them were specific to each country. Fragmented care and poor communication in the care trajectory hindered follow-up and continuity of care. CONCLUSIONS: Deprescribing interventions should include context-sensitive strategies to promote patient participation in SDM, foster the use of guidelines, and enhance interprofessional collaboration along the care trajectory. Care trajectory maps can be used as an implementation tool to translate BZRA deprescribing interventions into routine practice.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".