Bibliographic record
Abstract
Introduction: Deprescribing has been proposed to address the growing problem of polypharmacy. A local study involving patients attending public primary healthcare clinics in Singapore showed that majority of the patients were keen to have medications deprescribed if deemed appropriate by their prescriber. A survey of Vancouver physicians however showed that while most doctors were keen to deprescribe medications, prescription by another healthcare specialist was a barrier towards deprescribing. While this study was conducted overseas, there is paucity of literature available on the attitudes of doctors towards deprescribing in the local context. Hence this study aimed to elucidate physicians’ attitudes towards deprescribing. Methods: The study was conducted in Tan Tock Seng Hospital (TTSH) from October 2017 to March 2018. Physicians in the department of Geriatric Medicine (GRM) and Internal Medicine (IM) were recruited. A self-developed questionnaire was used for the study, which was developed after reviewing the literature on prescribers’ attitudes towards deprescribing. Survey questions were grouped into four themes: attitudes towards deprescribing, ability and skills to deprescribe, environment or work culture-related barriers, and patient-related barriers towards deprescribing. Results: A total of 80 physicians completed the questionnaire (38 from the department of GRM and 42 from IM). Most (98%) felt that deprescribing is beneficial for their patients, and 86% reported being motivated to deprescribe. Physicians reported being most comfortable deprescribing statins, and least comfortable deprescribing antiplatelets or anticoagulants. Physicians generally felt that there is a lack of guidelines or training to assist them in deprescribing (58%), which corresponded to the finding that provision of training or guidelines would help physicians deprescribe confidently (70% and 83% respectively). The most commonly cited work culture barrier was the hesitancy to stop medications prescribed by other healthcare professionals (84%). Difficulty and time required to explain to patients or their caregivers the rationale of deprescribing was the most common patient-related barrier (36%). Conclusion: ur study has shown that physicians are willing to deprescribe medications for their patients. Efforts can be done in raising awareness on the availability of deprescribing guidelines, as well as potential collaboration with physicians to adapt these guidelines to the local context.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".