An Asthma Specialist’s Consult Letter: What Do Parents Think About Receiving a Copy?
Bibliographic record
Abstract
Israel Amirav,1,* Virginia Vandall-Walker,2,3,* Jananee Rasiah,2,3 Laura Saunders,4 Harsha Belur,4 Brenda Sahlin,4 Mary Roduta Roberts,5 Dorit Redlich-Amirav6 1Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada; 2Patient Engagement Platform, Alberta SPOR Support Unit, Edmonton, Alberta, Canada; 3Faculty of Health Disciplines, Athabasca University, Athabasca & Faculty of Nursing, University of Alberta, Edmonton, Alberta, Canada; 4Parent Advisory Council, Edmonton, Alberta, Canada; 5Department of Occupational Therapy, University of Alberta, Edmonton, Alberta, Canada; 6Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, Canada*These authors contributed equally to this workCorrespondence: Israel AmiravPediatric Pulmonology Unit, Dana-Dwek Children’s Hospital, Faculty of Medicine, Tel Aviv University, Tel Aviv, IsraelTel +1 972-55-664-9359Email amirav@ualberta.caObjective: Written summaries about visits with general practitioners’ have influenced increased patient knowledge, satisfaction, recollection, and compliance, and strengthened the doctor–patient relationship. All previous studies about this communication pre-dated the electronic medical record (EMR) era, and none examined views from parents of children with asthma. We explored parents’ perceptions about receiving a hard copy summary Letter immediately following the visit, with the pediatric asthma specialist about findings and the care plan for their child.Methods: A Parent Advisory Council helped inform this pilot study, an observational cross-sectional electronic survey. Each Letter included a comprehensive summary of the child’s visit with the specialist.Results: Previous findings from patients about the benefits of receiving this Letter were strongly supported by data from 51 participants. Interestingly, more than 54% of respondents preferred receiving a hard copy Letter over an electronic copy, and 98% wanted other clinicians to adopt this practice.Conclusion: Parents of children with asthma value and want timely written information of this nature from other clinicians.Practice Implications: These results can influence further asthma research to promote a change in the perceptions of clinicians, parents, and patients about timely access to health information in the EMR era.Keywords: patient perspectives, provider–patient communication, asthma, parents
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".