Shifting asthma management with Asthma Right Care communication tools
Bibliographic record
Abstract
1. Introduction Currently, 60% of patients with asthma do not receive basic care and up to 70% of them take the wrong medication or have poor technique. Asthma Right Care is a global social movement led by the International Primary Care Respiratory Group (IPCRG) to achieve a real change in asthma care worldwide. 2. Aims Finding solutions within the real-life context of health systems to improve asthma care adding value to interventions. 3. Methods IPCRG set up a multi-national Delivery Team from four pilot countries – Canada, Portugal, Spain and the UK – including patients, pharmacists, GPs and nurses who helped to create several tools for different settings: primary care, community pharmacy and emergency care. Focused on going deeper in paradigm and behaviour shift, they designed and tested a three-language set of highly effective conversation starters on over-reliance on symptom relief, particularly using short-acting beta-2-agonists for asthma management. 4. Results Three Asthma Right Care tools (https://www.ipcrg.org/asthma-right-care-key-resources) were created to promote conversations about SABA use between asthma patients and healthcare professionals as partners, making every contact count: a) Question and challenge cards, which are useful for icebreakers, chats, discussion and social media. b) Asthma SABA Slide rule, which determines how many puffs of SABA inhaler the patient is taking or how many the healthcare professional is prescribing. c) Reliever Reliance Test, which allows patients to identify and change beliefs that drive them to SABA over-reliance. 5. Conclusions Asthma Right Care tools differ from a standard education or communication campaign, challenging new ways to build up confidence and competence.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".