Evaluation of a Web-Based Malaria Education Module for Hospital Pharmacists
Notice bibliographique
Résumé
Malaria is a serious tropical disease that is an increasing problem for many hospitals across Canada. Between 370 and 460 cases are reported annually in this country,1 and these numbers are known to underestimate the true incidence by 30% to 50%.2 Between 2001 and 2006, a mean of 17.2 cases of severe or complicated malaria were reported to the Canadian Malaria Network each year.3 This rate may rise with increases in the number of people at risk for malaria and the number with the disease who actually present to the Canadian health care system for care. Increases in the numbers of immigrants, refugees, travellers, and military personnel returning from service overseas are some possible contributors to this trend. Locally, cases reported in the Calgary Health Region may account for 4% to 8% of the national caseload. The drug of choice for severe malaria in Canada (quinine, administered intravenously) has restricted availability and can be obtained only through 13 repository sites designated by the Canadian Malaria Network. These repositories are located in all provinces except New Brunswick and Prince Edward Island; no repositories are located in the Northwest Territories, Yukon Territory, or Nunavut. Most cases of malaria in the Calgary Health Region are caused by the potentially deadly parasitic species Plasmodium falciparum; such infections can progress to organ failure within 24 to 48 h after introduction to the bloodstream. For people infected with P. falciparum, timely recognition and appropriate antimalarial treatment are essential to minimize associated morbidity and mortality.4 Yet, in a review of 100 consecutive malaria cases in the greater Toronto area, 23% of the patients had delays of greater than 6 h before initiation of antimalarial therapy (after receipt of a laboratoryconfirmed diagnosis), and 36% of patients infected with P. falciparum had no follow-up within the first 4 days of therapy.4 Incorrect treatment regimens and errors in initial management of severe malaria were 2 “major errors” cited. Nationally, the Canadian Malaria Network reported treatment delays of more than 24 h in 17.5% of severe or complicated malaria cases over the period 2001 to 2006.3 These factors led pharmacy department staff of the Calgary Health Region to ask, “How prepared are pharmacists to deal with uncommon yet important malaria orders in the dispensary?” Observations in acute care dispensaries in the Calgary Health Region revealed that malaria resources (e.g., hard-copy publications, bookmarked websites) at individual workstations were limited and that few pharmacists knew that there was an infectious diseases consultant with advanced training in travel and tropical health on staff. Importantly, several drug information sources routinely used by pharmacists in the Calgary Health Region are published in the United States, where quinidine for IV administration is available and is currently used as first-line treatment for severe malaria. Use of such references could lead to discrepancies or errors in dosing under the current Canadian guidelines. Published evaluations have shown positive results of using web-based media to educate pharmacists and pharmacy students.5-7 This literature, along with local confusion and delays in treatment experienced within the Calgary Health Region, prompted the development of a malaria education initiative for pharmacists that was disseminated through the region’s internal pharmacy website. The module was intended to support inpatient dispensary pharmacists in the management of malaria and to aid in the timely administration of antimalarial therapy. This study was undertaken to evaluate the effectiveness of the module in making pharmacists more knowledgeable about the triage process for malaria, regional procedures concerning supplies of quinine for IV administration, and the locations of standardized malaria information. The secondary objectives were to evaluate pharmacists’ comfort levels in processing malaria orders and user satisfaction with respect to the web-based education module.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,008 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,009 |
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 ».