Scale Evaluating the Information Literacy Self-Efficacy of Medical Students Created and Tested in a Six-Year Belgian Medical Program
Notice bibliographique
Résumé
A Review of: De Meulemeester, A., Buysse, H., & Peleman, R. (2018). Development and validation of an Information Literacy Self-Efficacy Scale for medical students. Journal of Information Literacy, 12(1), 27-47. Retrieved from https://ojs.lboro.ac.uk/JIL/article/view/PRA-V12-I1-2 Abstract Objective – To create and validate a scale evaluating the information literacy (IL) self-efficacy beliefs of medical students. Design – Scale development. Setting – Large, public research university in Belgium. Subjects – 1,252 medical students enrolled in a six-year medical program in the 2013-2014 academic year. Methods – Ten medical-specific IL self-efficacy questions were developed to expand a 28-item Information Literacy Self-Efficacy Scale (ILSES) (Kurbanoglu, Akkoyunlu, & Umay, 2006). Medical students in Years 1 – 5 completed the questionnaire (in English) in the first two weeks of the academic year, with students in Year 6 completing after final exams. Respondents rated their confidence with each item 0 (‘I do not feel confident at all’) to 100 (‘I feel 100% confident’). Principal Axis Factoring analysis was conducted on all 38 items to identify subscales. Responses were found suitable for factor analysis using Bartlett’s Test of Sphericity and the Kaiser-Meyer-Olkin measure (KMO). Factors were extracted using the Kaiser-Gutmann rule with Varimax rotation applied. Cronbach’s alpha was used to test the internal consistency of each identified subscale. Following a One-way-ANOVA testing for significant differences, a Tamhane T2 post-hoc test obtained a pairwise comparison between mean responses for each student year. Main Results – Five subscales with a total of 35 items were validated for inclusion in the Information Literacy Self-Efficacy Scale for Medicine (ILSES-M) and found to have a high reliability (Chronbach’s alpha scores greater than .70). Subscales were labelled by concept, including “Evaluating and Processing Information” (11 items), “Medical Information Literacy Skills” (10 items), “Searching and Finding Information” (6 items), “Using the Library” (4 items), and “Bibliography” (4 items). The factor loading of non-medical subscales closely reflected studies validating the original ILSES (Kurbanoglu, Akkoyunla, & Umay, 2006; Usluel, 2007), suggesting consistency in varying contexts and across time. Although overall subscale means were relatively low, immediate findings among medical students at Ghent University demonstrated an increase in the IL self-efficacy of students as they advance through the 6-year medical program. Students revealed the least confidence in “Using the Library.” Conclusions – The self-efficacy of individuals in approaching IL tasks has an impact on self-motivation and lifelong learning. The authors developed the ILSES-M as part of a longitudinal study protocol appraising the IL self-efficacy beliefs of students in a six-year medical curriculum (De Meulemeester, Peleman, & Buysse, 2018). The ILSES-M “…could give a clear idea about the evolution of perceived IL and the related need for support and training” (p. 43). Further research could evaluate the scale’s impact on curriculum and, conversely, the impact of curricular changes on ILSE. Qualitative research may afford additional context for scale interpretation. The scale may also provide opportunities to assess the confidence levels of incoming students throughout time. The authors suggested further research should apply the ILSES-M in diverse cultural and curricular settings.
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 | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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.
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 ».