Information Horizons Mapping is Related to Other Measures of Health Literacy but Not Information Literacy
Notice bibliographique
Résumé
A Review of: Zimmerman, M.S. (2020). Mapping literacies: Comparing information horizons mapping to measures of information and health literacy. Journal of Documentation, 76(2), 531–551. https://doi.org/10.1108/JD-05-2019-0090 Abstract Objective – To evaluate information horizons mapping as a valid measure for assessing information literacy and health literacy compared to three validated information and health literacy measurements and level of educational attainment. Design – Quantitative data analysis using multiple regression and the Anker, Reinhart, and Feeley model as the conceptual framework. Setting – A small university-centered community in Iowa City. Subjects – 149 members of the university community. Methods – The author conducted a power analysis to determine a minimum sample size required for maintaining study validity and selected the Anker Model of conceptual framing for health information-seeking behavior. This is a three-phased model that explores the information seeker’s predisposing characteristics, engagement in health information seeking, and outcomes associated with information seeking. Recruited participants completed three assessments—the Tool for Real-time Assessment of Information Literacy Skills (TRAILS), the Health Literacy Skills Instrument (HLSI), and the Brief Health Literacy Screen (BHLS)—and drew information horizon maps illustrating what sources of information they tend to seek for health-related questions. The author calculated information horizon map results using a scoring system incorporating the number and quality of information sources identified in the maps and applied multiple linear regression analysis and Spearman’s rank correlation coefficient to participants’ scores from all four assessments as well as their level of educational attainment to determine strengths of relationships between variables. Main Results – In the information horizons map results, participants identified an average of 6.9 information sources with a range of 3–13 and received an average score of 18.8 in information source quality with a range of 4–45. The author applied multiple linear regression to predict the number of information source counts on the information horizons map based on HLSI, TRAILS, and BHLS assessment scores and level of educational attainment and found a significant relationship (p=0.044). A significant relationship also existed between quality of source scores on the map based on HLSI, TRAILS, and BHLS assessment scores and level of educational attainment (p=0.033). Removing the educational attainment variable produced an even stronger significant result. Spearman’s rank correlation coefficient supported the findings of the multiple regression analysis and revealed a strong relationship between source count and scores on the BHLS (r=0.87) and HLSI (r=71) but a weak relationship between source counts and TRAILS score and level of educational attainment. Source quality had a weak relationship with BHLS scores (r=0.24), a moderate relationship with the HLSI scores (r=0.50), and a weak relationship with TRAILS scores and educational attainment. Conclusions – The data analysis suggests a significant relationship between information horizons mapping and health literacy but not information literacy or level of educational attainment. This data supports findings from the author’s previous research examining the relationship between information horizon maps and information literacy scores for refugee and immigrant women. It also suggests that information horizons mapping may facilitate storytelling that reflects the complexity of participants’ health literacy ability and may introduce the potential to assess low-literacy level populations. More research is needed to examine the quality and complexity produced in information horizons maps. This methodology may be applied to investigate better techniques for assessing the health literacy levels among populations that struggle with prose-based assessments.
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 | 0,021 | 0,163 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».