Checking Inhaler Technique in the Community Pharmacy: Predictors of Critical Errors
Bibliographic record
Abstract
Inhaled medications are critical in the pharmaceutical management of respiratory conditions, however, the majority of patients demonstrate at least one critical error when using an inhaler. Since community pharmacists can be instrumental in addressing this care gap, we aimed to determine the rate and type of critical inhaler errors in community pharmacy settings, elucidate the factors contributing to inhaler technique errors, and identify instances when community pharmacists check proper inhaler use. Fourth year pharmacy students on community practice placement (n = 53) identified 200 patients where at least one error was observed in 78% of participants when demonstrating inhaler technique. Prevalent errors of the users were associated with metered dose inhaler (MDI) (55.6%), Ellipta® (88.3%), and Discus® (86.7%) devices. Overall, the mean number of errors was 1.09. Possession of more than one inhaler, use of rescue inhaler, and poor control of asthma were found to be significant predictors of having at least one critical error. In all participating pharmacies, inhaler technique is mainly checked on patient request (93.0%) and for all new inhalers (79.0%).
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".