How we teach children with asthma to use their inhaler: A scoping review protocol
Bibliographic record
Abstract
Abstract Background One reason that asthma remains poorly controlled in children is poor inhaler technique. Current guidelines recommend checking inhaler technique at each clinical visit. However, they do not specify how best to train children to mastery of correct inhaler technique. Currently many children are simply shown how to use inhalers (brief Instruction) which results in less than 50% with correct inhaler technique. The aim of this scoping review is to explore published literature on teaching methods used to train children to master correct inhaler technique. Methods This scoping review will follow the Arksey O'Malley scoping review methodology and the 2015 Joanna Briggs Institute guidelines. It will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols extension for Scoping Reviews. An initial pilot exercise will be undertaken using the online database MEDLINE before proceeding to a complete search using the databases Embase, Scopus, Web of Science, CINAHL and the Cochrane library. We will include studies published since the year 1956 on teaching the skill of inhaler technique to children. Due to the iterative nature of a scoping review, the research question or the search terms may alter following a piloting process. Discussion This scoping review will provide a broad overview of currently used educational techniques to improve inhaler technique in children with asthma. The analysis will allow us to refine future research in this area by focusing on the most effective techniques and optimising them. This will likely lead to a systematic review of the literature with the potential to design a randomised control trial of educational interventions to teach inhaler technique to children with asthma.
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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 | 0.099 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.017 |
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".