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Record W2951905814 · doi:10.18438/eblip29564

Scale Evaluating the Information Literacy Self-Efficacy of Medical Students Created and Tested in a Six-Year Belgian Medical Program

2019· article· en· W2951905814 on OpenAlexvenueno aff
Brittany Richardson

Bibliographic record

VenueEvidence Based Library and Information Practice · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaVarimax rotationMedical educationTest (biology)Scale (ratio)PsychologyRespondentLiteracyLikert scaleMedicineFamily medicinePsychometricsClinical psychologyPedagogy

Abstract

fetched live from OpenAlex

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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.497
Teacher spread0.447 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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