Challenges and Enablers of Deprescribing: A General Practitioner Perspective
Bibliographic record
Abstract
AIMS: Deprescribing is the process of reducing or discontinuing medicines that are unnecessary or deemed to be harmful. We aimed to investigate general practitioner (GP) perceived challenges to deprescribing in residential care and the possible enablers that support GPs to implement deprescribing. METHODS: A qualitative study was undertaken using semi-structured, face-to-face interviews from two cities in New Zealand and a purpose-developed pilot-tested interview schedule. Interviews were recorded with permission and transcribed verbatim. Transcripts were read and re-read and themes were identified with iterative building of a coding list until all data was accounted for. Interviews continued until saturation of ideas occurred. Analysis was carried out with the assistance of a Theoretical Domains Framework (TDF) and constant comparison techniques. Several themes were identified. Challenges and enablers of deprescribing were determined based on participants' answers. RESULTS: Ten GPs agreed to participate. Four themes were identified to define the issues around prescribing for older people, from the GPs' perspectives. Theme 1, the 'recognition of the problem', discusses the difficulties involved with prescribing for older people. Theme 2 outlines the identified behaviour change factors relevant to the problem. Deprescribing challenges were drawn from these factors and summarised in Theme 3 under three major headings; 'prescribing factors', 'social influences' and 'policy and processes'. Deprescribing enablers, based on the opinions and professional experience of GPs, were retrieved and summarised in Theme 4. CONCLUSION: The process of deprescribing is laced with many challenges for GPs. The uncertainty of research evidence in older people and social factors such as specialists' and nurses' influences were among the major challenges identified. Deprescribing enablers encompassed support for GPs' awareness and knowledge, improvement of communication between multiple prescribers, adequate reimbursement and pharmacists being involved in the multidisciplinary team.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".