Qualitative study on shared decision making in cystitis management in general practice
Bibliographic record
Abstract
BACKGROUND: Cystitis is commonly treated with antibiotics, although non-antibiotic options could be considered for healthy non-pregnant women. Shared decision making (SDM) can be used in cystitis management to discuss the various treatment options but is not frequently applied in general practice. AIM: To identify barriers and facilitators for applying SDM in cystitis management in general practice. DESIGN & SETTING: Qualitative explorative research in general practice with healthcare professionals (HCPs; GPs and GP assistants) and healthy non-pregnant women with a recent history of cystitis (patients). METHOD: Individual semi-structured interviews were conducted between May and October 2022. We applied a combination of thematic and framework analysis. RESULTS: Ten GPs, seven GP assistants, and 15 patients were interviewed. We identified the following three main barriers and one key facilitator: (1) applying SDM is deemed inefficient; (2) HCPs assume that patients expect antibiotic treatment and some HCPs consider non-antibiotic treatment inferior; (3) patients are largely unaware of the various non-antibiotic treatment options for cystitis; and (4) HCPs recognise some benefits of applying SDM in cystitis management, including reduced antibiotic use and improved patient empowerment, and patients appreciate involvement in treatment decisions, but preferences for SDM vary. CONCLUSION: SDM is infrequently applied in cystitis treatment in general practice owing to the current focus on efficient cystitis management that omits patient contact, HCPs' perceptions, and patient unawareness. Nevertheless, both HCPs and patients recognise the long-term benefits of applying SDM in cystitis management. Our findings facilitate the development of tailored interventions to increase the application of SDM, which should be co-created with HCPs and patients, and fit into the current efficient cystitis management.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".