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Enregistrement W2297836712 · doi:10.18438/b8x91m

Librarian-Led Information Literacy Training Delivered in Small Groups Improved Medical Students’ Confidence in Their Ability to Use Evidence Based Resources Effectively

2016· article· en· W2297836712 sur OpenAlexvenueaboutno aff
Elizabeth Stovold

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2016
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Sciences Research and Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInformation literacyMedical educationSession (web analytics)Intervention (counseling)Health literacyLibrary instructionPsychologyMedicineHealth careNursingComputer sciencePedagogy

Résumé

récupéré en direct d'OpenAlex

A Review of: McClurg, C., Powelson, S., Lang, E., Aghajafari, F., & Edworthy, S. (2015). Evaluating effectiveness of small group information literacy instruction for Undergraduate Medical Education students using a pre- and post-survey study design. Health Information & Libraries Journal. 32(2), 120-130. http://doi.org/10.1111/hir.12098 Abstract Objective – To assess the effectiveness of librarian-led small group information literacy sessions, which were integrated into the second year of a three-year undergraduate medical course. Design – A pre- and post-intervention survey questionnaire. Setting – A large university in Canada. Subjects – A cohort of 160 second year undergraduate medical students enrolled in the three-year programme of a large university in Canada. Methods – As part of the redevelopment of the undergraduate three-year medical course, information literacy skills in evidence based medicine were integrated into the seminar and small group teaching programme. Every week for five weeks, 3 librarians each visited 4 small groups of 15 students to deliver a 15-minute session as part of a 2-hour long seminar led by practising physicians. The sessions did not include a formal hands-on component, however, students were encouraged to try out searches on their own devices. Each 15-minute session covered 3 learning objectives, including how to use PubMed clinical queries, how to use MeSH, and how to search for systematic reviews and guidelines. A pre- and post-intervention survey design was used to assess students’ perceptions of the impact of these sessions. The students were asked to complete an online Survey Monkey survey before and after the five week lecture block. The questions covered resource selection, perception of barriers to finding evidence based information, and the students’ confidence in using evidence based resources. The data were analysed descriptively. Main results – The pre-survey achieved a 90% (144/160) response rate while the post-survey achieved a 75% (112/160) response rate. The post-survey indicated an increase in the likelihood that students would use Ovid MEDLINE, carry out a literature search, and consult a librarian, with a decrease in those who would consult a print or online textbook. There was limited change in the students’ confidence that they could find answers quickly, but more of an increase in the proportion of students who were confident they could find systematic reviews and guidelines, and use search limits, PICO, and MeSH. Before the intervention, “knowing where to search,” devising a search strategy, and retrieving too many results were all thought to be obstacles by the students. After the small group training, students considered these issues less of a problem. The post-survey also included an opportunity for the students to comment on their experience with the programme overall. Of the 54 responses received, 34 identified the library component as being the most important thing they had learned in the small group part of the course. Conclusion – The authors conclude that integrating information literacy into the undergraduate curriculum as part of the small group seminar series is effective. They suggest future directions for research, such as a study to assess the impact of the training on specific skills rather than student confidence and evaluations of other teaching methods.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,026

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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 Opus0,077
Tête enseignante GPT0,405
Écart entre enseignants0,328 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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
Publié2016
Routes d'admission2
Résumé présentoui

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