Food allergy management from the perspective of patients or caregivers, and allergists: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Research has shown that the long term management of food allergy is suboptimal. Our study aims to provide direction for improvement, by evaluating food allergy management from the perspective of, food allergic patients or their caregivers, and allergists in selected outpatient settings in Ontario. METHODS: This two-part study included an anonymous questionnaire completed by patients or their caregivers in allergy clinics, and a qualitative interview with allergists. In Part A, food allergic patients or their caregivers were surveyed about information they received on food allergy, their level of confidence with self-management, and their learning needs. In Part B, allergists were interviewed about teaching priorities and the challenges and strategies that currently exist in food allergy management. The questionnaire was developed and piloted at the Hamilton Health Sciences Corporation-McMaster University Medical Center Site. Using convenience sampling, participants were recruited from 6 allergy clinics in 5 Ontario cities. Patients of any age with food allergy who were evaluated by an allergist were considered for inclusion. Quantitative data was analyzed using descriptive statistics and frequency analysis. Audio recorded interviews with allergists were transcribed verbatim and analyzed using content analysis of grounded theory methodology. RESULTS: Ninety-two food allergic families in the care of 6 allergists in Toronto, Hamilton, London, Kitchener, and Kingston participated in the study. Key areas requiring improvement in food allergy management were identified: 33% of families were not shown how to use an epinephrine auto-injector with a trainer, only 57% were asked to demonstrate an auto-injector, despite being on average at their 5th visit, and only about 30% felt very confident about when and how to give an auto-injector. Fifty percent of families did not receive sufficient information on medical identification and 21% did not receive information about support groups. Interviews with allergists revealed limitations in time and nursing resources. CONCLUSIONS: Our study highlights the educational gaps and overall experiences of food allergic families in Ontario, and the challenges faced by the allergists managing them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".