Perceptions of Community Pharmacy Staff regarding Strategies to Reduce and to Improve the Reporting of Medication Incidents
Notice bibliographique
Résumé
Background: Medication incidents can have serious consequences for the health of patients and the perceived safety of community pharmacy practice. This study was designed to assess the perceptions of pharmacy staff members about various strategies for reducing and improving the reporting of medications incidents, with the ultimate goal of helping pharmacy managers to determine which practices are likely to be widely accepted. Methods: Staff members of community pharmacies were recruited from 13 pharmacies in Nova Scotia. This convenience sample consisted of pharmacists, pharmacy managers and owners, and pharmacy support staff (i.e., technicians, interns and pharmacy students). The questionnaire had 5 sections, including sections on demographic characteristics, organizational culture within the pharmacies, strategies for reducing and for improving the reporting of medication incidents and existing reporting processes and desired changes, along with an open-ended section on reporting of medication incidents in general. The current article reports data from the 20-question section that sought respondents' perceptions, according to a Likert-type scale, of selected practices for reducing medication incidents and for identifying and disclosing any such incidents that do occur. Multivariate analysis of variance (MANOVA) was used to examine overall differences in mean ratings for various strategies by staff group or ownership type. Follow-up univariate analysis and Tukey test were used to examine differences in staff groups and ownership types for each reduction and reporting strategy. Correlation analyses were performed to determine strategies for which individual respondents had similar perceptions. Only pairs of strategies with moderate or strong correlations ( r ≥ 0.60) are discussed. Results: MANOVA indicated significant differences in the perceived effectiveness of various strategies for reducing and for improving the reporting of medication incidents. Respondents indicated that having clinical pharmacists help physicians to select drug therapies would be the most effective strategy to reduce the frequency of medication incidents, and they thought that sharing with colleagues any lessons learned from incidents that did occur and assuring anonymity of reporting would be the most effective ways to increase the reporting of medication incidents. Conclusions: Instituting strategies for reducing and enhancing the reporting of medication incidents that are viewed as effective by pharmacy staff members may help to increase reporting rates and to reduce the number of such incidents occurring at the community pharmacy.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».