Discontinuing Benzodiazepine Therapy: An Interdisciplinary Approach at a Geriatric Day Hospital
Bibliographic record
Abstract
Background: Despite the known adverse effects of benzodiazepines, elderly people commonly use these drugs over long periods to treat insomnia and anxiety. This qualitative study was conducted to examine the experiences of patients and care providers in a geriatric day hospital (GDH) as patients participated in a benzodiazepine tapering process, to identify the components and processes of the benzodiazepine tapering intervention, and to begin exploring how they influence patient outcomes. Methods: The study was conducted in a GDH in a Canadian city. Data were gathered from a discussion group and from individual semistructured interviews with 13 health care providers and 5 patients. Charts were reviewed to gather demographic data and confirm provider activities. A reflexive approach was conducted whereby each care provider reviewed and modified the role description created from information provided during his or her interview. Themes were determined through constant comparative analysis of transcripts, which included 5 meetings of the research team. Results: The tapering of benzodiazepines at the GDH was effected primarily by 3 people: the physician, the pharmacist and the nurse. Other members of the interdisciplinary team were not always aware of which patients were tapering their benzodiazepine therapy, but they supported patients in a variety of ways. The patients included in this analysis were willing to taper their benzodiazepines and did not consider the experience significant in any way. Conclusion: The health care provider roles, processes and tools described here could be replicated in other environments to assist patients who are tapering benzodiazepine therapy. Further research is needed to understand the interrelationships of all components of GDH care to determine their relative importance in facilitating behaviour change related to benzodiazepine tapering.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".