A survey of primary care patients’ readiness to engage in the de-adoption practices recommended by Choosing Wisely Canada
Bibliographic record
Abstract
BACKGROUND: Strategies such as Choosing Wisely have been established to identify the overuse of interventions considered as low-value. Reduction of low-value practices will require patients to understand why certain interventions are no longer recommended. The objective of this study was to determine whether older adults accept the rationale for and perceive themselves ready to de-adopt annual electrocardiogram testing, imaging for low back pain, the use of antibiotics for sinusitis, the use of sedative-hypnotics for insomnia, and the use of antipsychotics to treat behavioural symptoms of dementia. METHODS: A self-administered iPad survey was distributed to consecutive patients aged 50 years and older, presenting to three primary care outpatient practices in Ontario, Canada. Data from patients who were able and willing to complete the survey while waiting to see their physician were included. The survey queried knowledge, attitudes and behaviours around the targeted low-value interventions, before and after exposure to a Choosing Wisely Canada patient educational brochure on one of these five topics. A subset of patients agreed to participate in a semi-structured interview after their clinic visit. RESULTS: Three-hundred and forty-four patients (mean age 63, range 50-88, 59 % female) read the materials and completed the survey. Forty-eight percent (95 % CI 43-53 %) intended to discuss the information with a healthcare provider. Forty-five percent (95 % CI 40-51 %) expressed a desire to change current low-value practices. Approximately two-thirds of those who indicated they would not change future behaviours explained that it was because they were already espousing the Choosing Wisely values. After reading the Choosing Wisely brochures, knowledge improved independent of age, sex and education in 48 % (95 % CI 38-57 %) of participants about electrocardiogram testing, in 74 % (95 % CI 65-82 %) about use of antipsychotics, in 66 % (95 % CI 52-78 %) about use of antibiotics for sinusitis, in 60 % (95 % CI 46-72 %) about imaging for low back pain, and in 40 % (95 % CI 26-55 %) about sedative-hypnotic use in the elderly. CONCLUSIONS: The majority of primary care patients seem ready to de-adopt low-value practices. Provision of education in clinic waiting rooms can help improve knowledge around unnecessary care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".