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Record W2763282873 · doi:10.1093/pch/20.5.e84a

138: Prescribing Long-Term Asthma Controller in Poorly Controlled Children with Persistent Asthma: Reported Behaviour, Facilitators and Solutions

2015· article· en· W2763282873 on OpenAlexaffabout
FM Ducharme, AJ Lamontagne, Sandra Peláez, Roni Grad, Kim Lavoie, Peter Ernst, ML McKinney, Simon Bacon, Hélène Guay, Johanne Collin, Lucie Blais

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineAsthmaMedical prescriptionFamily medicinePediatricsFocus groupEmergency departmentVignetteNursingInternal medicine

Abstract

fetched live from OpenAlex

Despite clear guidelines, many school-aged children with persistent asthma are not prescribed long-term asthma controller by their physician. To identify of key facilitators and solutions proposed by physicians to facilitate long-term prescription of asthma controller in children with persistent asthma (for ≥3 months or until follow-up with the treating physician). We designed a two-phase mixed methods study. Following a qualitative study in which physicians proposed numerous facilitators and solutions to increase the prescription of long-term asthma controller, we developed a quantitative 175 item-questionnaire seeking physicians' endorsement of proposed solutions and likely behaviour in one of four case vignettes of an individual (school-aged child or adult) with poor asthma control seen in the acute care or clinic setting. Using the Tailored design method, the questionnaire was sent to randomly-selected paediatricians (n=209), general practitioners (GP, n=525) and emergency physicians (EP, n=103), representing respectively 29%, 6% and 73% of the physician census tract of the College des médecins du Québec. Results are presented as weighted proportions. We focus this report on responses to the pediatric cases. After excluding 89 non-eligible physicians, we obtained a 56% participation rate (n=421) with 212 (115 paediatricians, 79 GP, and 18 EP) selecting a paediatric vignette (58% acute care; 42% clinic setting). Participants were predominantly women (79%), paediatricians (54.2%), practicing for a median of 11.5 years in an academic (53%) environment. Most physicians correctly identified the child as having uncontrolled (72%), persistent (81%) asthma and would have prescribed a long-term asthma controller (83%). The following resources greatly increased physicians' comfort in prescribing a long-term asthma controller: patient asthma education, use of lung function testing, concurring opinion from and access to specialists, frequent medical follow-ups, and the implication of allied healthcare professionals in patient self-management. Physicians highly endorsed proposals for: inter-professional team approach to patient self-management, professional education on asthma management, memory aids, computerized decision support tools, and computerized systems of waiting time for various health referrals. A large majority of surveyed physicians reported prescribing long-term asthma controller to the uncontrolled patient in the selected vignette, attesting to their general intention. An inter-professional team approach to guide patient self-management with allied health professionals, physician educational sessions, patient asthma education, decision support tool, and computerized waiting time for referrals were highly favoured by physicians treating children with asthma.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.046
GPT teacher head0.328
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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