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Enregistrement W7008666784

Comparison of Two Methods of Needs Assessment in Continuing Pharmacy Education

2023· dissertation· en· W7008666784 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Langueen
DomaineMedicine
ThématiquePharmaceutical Practices and Patient Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNeeds assessmentPharmacyContinuing educationLikert scaleVariety (cybernetics)Pharmacy practiceScale (ratio)Needs analysisInformation needsSurvey instrument
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Identifying educational needs is recognized as an important part of planning continuing education programs. There are a variety of ways of assessing educational needs. In Pharmacy, needs assessment methods have ranged from those which are relatively subjective (i.e., pharmacists' perceived needs and planners' intuitive feelings) to those which are relatively objective (i.e., knowledge-based or test-derived needs). A more complete understanding of the relative worth of various methods used to assess educational needs is required if continuing pharmacy education (C.P.E.) programs are to be focused on the real problems of potential learners.\n\nThe main purpose of the present study was to compare two methods of needs assessments in continuing pharmacy education. The Canadian Consensus on Asthma Management Guidelines were used as the framework to develop two needs assessment instruments. Perceived needs assessment, or " what pharmacists think they need to learn", was undertaken using a questionnaire which asked pharmacists to rate their degree of educational needs on a five-point Likert scale where 1=low need and 5 =high need. Knowledge-based needs were assessed by means of an examination containing 39 questions to measure the level of knowledge of the same pharmacists regarding care of asthma patients, prior to their participation in a C.P.E. program on asthma. Participants' learning was assessed after the C.P.E. program by the administration of a post-test containing 21 of the pre-test exam questions.\n\nA total of 113 pharmacists who attended the C.P.E. program in either Regina or Saskatoon, Saskatchewan in May 1996, participated in this study. Data analysis indicated there were no significant correlations between perceived needs and knowledge-based needs among the pharmacists. When various subgroups of pharmacists were compared, there were no significant differences found among groups in the areas of perceived needs and post-test outcomes. However, several groups of pharmacists differed significantly in their knowledge-based needs (i.e., pre-test results). Variables which showed such differences included: gender, year of graduation, employment status, employment position, time spent in management activities, and family history of asthma. After further factor analysis, no correlations were found between overall perceived needs and knowledge-based needs. However, the factor analysis articulated significant differences between perceived needs and knowledge-based needs in the medication domain. Factor analysis also determined that certain subgroups of pharmacists differed in knowledge-based needs in the domains of pathophysiology and medication.\n\nEffectiveness of the C.P.E. program planned based on the intuitive feelings of the planners was assessed by comparing scores of paired pre-test and post-test items. Significant positive changes in learning were found on the post-test. A simple tabulation of data also showed an inclination that when pharmacists were aware of their own needs, effective learning took place.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,737
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,070
Tête enseignante GPT0,417
Écart entre enseignants0,348 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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