Patient engagement and patient support programs in allergy immunotherapy: a call to action for improving long-term adherence
Bibliographic record
Abstract
Allergy immunotherapy (AIT) is acknowledged to produce beneficial mid- and long-term clinical and immunologic effects and increased quality of life in patients with allergic respiratory diseases (such as allergic rhinoconjunctivitis and allergic asthma). However, poor adherence to AIT (due to intentional and/or non-intentional factors) is still a barrier to achieving these benefits. There is an urgent need for patient support programs (PSPs) that encompass communication, educational and motivational components. In the field of AIT, a PSP should be capable of (1) improving adherence, (2) boosting patient engagement, (3) explaining how AIT differs from pharmacological allergy treatments; (4) increasing health literacy about chronic, progressive, immunoglobulin-E-mediated immune diseases, (5) helping the patient to understand and manage local or systemic adverse events, and (6) providing and/or predicting local data on aeroallergen levels. We reviewed the literature in this field and have identified a number of practical issues to be addressed when implementing a PSP for AIT: the measurement of adherence, the choice of technologies, reminders, communication channels and content, the use of "push" messaging and social networks, interactivity, and the involvement of caregivers and patient leaders. A key issue is "hi-tech" (i.e. approaches based mainly on information technology) vs. "hi-touch" (based mainly on interaction with humans, i.e. family members, patient mentors and healthcare professionals). We conclude that multistakeholder PSPs (combining patient-, provider and society-based actions) must now be developed and tested with a view to increasing adherence, efficacy and safety in the field of AIT.
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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 | 0.043 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".