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Enregistrement W1488783403 · doi:10.18438/b8cp6b

Public Library Training Program for Older Adults Addresses Their Computer and Health Literacy Needs

2012· article· en· W1488783403 sur OpenAlexaffvenue
Cari Merkley

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2012
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueTechnology Use by Older Adults
Établissements canadiensMount Royal University
Organismes subventionnairesnon disponible
Mots-clésThe InternetComputer literacyHealth literacyMedical educationIntervention (counseling)DemographicsScale (ratio)AnxietyMedicineGerontologyPsychologyFamily medicineHealth careNursingComputer scienceWorld Wide WebPsychiatry

Résumé

récupéré en direct d'OpenAlex

Objective – To evaluate the efficacy of an e-health literacy educational intervention aimed at older adults.
 
 Design – Pre and post intervention questionnaires administered in an experimental study.
 
 Setting – Two public library branches in Maryland.
 
 Subjects – 218 adults between 60 and 89 years of age.
 
 Methods – A convenience sample of older adults was recruited to participate in a four week training program structured around the National Institutes of Health toolkit Helping Older Adults Search for Health Information Online. During the program, classes met at the participating libraries twice a week. Sessions were two hours in length, and employed hands on exercises led by Master of Library Science students. The training included an introduction to the Internet, as well as in depth training in the use of the NIHSeniorHealth and MedlinePlus websites. In the first class, participants were asked to complete a pre-training questionnaire that included questions relating to demographics and previous computer and Internet experience, as well as measures from the Computer Anxiety Scale and two subscales of the Attitudes toward Computers Questionnaire. Participants between September 2008 and June 2009 also completed pre-training computer and web knowledge tests that asked individuals to label the parts of a computer and of a website using a provided list of terms. At the end of the program, participants were asked to complete post-training questionnaires that included the previously employed questions from the Computer Anxiety Scale and Attitudes towards Computer Questionnaire. New questions were added relating to the participants’ satisfaction with the training, its impact on their health decision making, their perceptions of public libraries, and the perceived usability and utility of the two websites highlighted during the training program. Those who completed pre-training knowledge tests were also asked to complete the same exercises at the end of the program. 
 
 Main Results – Participants showed significant decreases in their levels of computer anxiety, and significant increases in their interest in computers at the end of the program (p>0.01). Computer and web knowledge also increased among those completing the knowledge tests. Most participants (78%) indicated that something they had learned in the program impacted their health decision making, and just over half of respondents (55%) changed how they took medication as a result of the program. Participants were also very satisfied with the program’s delivery and format, with 97% indicating that they had learned a lot from the course. Most (68%) participants said that they wished the class had been longer, and there was full support for similar programming to be offered at public libraries. Participants also reported that they found the NIHSeniorHealth website more useful, but not significantly more usable, than MedlinePlus.
 
 Conclusion – The intervention as designed successfully addressed issues of computer and health literacy with older adult participants. By using existing resources, such as public library computer facilities and curricula developed by the National Institutes of Health, the intervention also provides a model that could be easily replicated in other locations without the need for significant financial resources.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,953
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,361
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,052
Tête enseignante GPT0,327
Écart entre enseignants0,275 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations4
Publié2012
Routes d'admission2
Résumé présentoui

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