Collaborative Learning is an Effective Method for Improving the E-health Literacy of Older Adults in the Community
Notice bibliographique
Résumé
Objective – To determine whether collaborative learning strategies in an informal class setting can improve electronic health literacy skills of older adults.
 
 Design – Pre- and post-test instruments used to measure effects of an educational intervention.
 
 Setting – Small group classes offered at two branches of a large, publicly funded, urban public library in Maryland.
 
 Subjects – A total of 111 adults aged 52 to 91, mean age 70.4 (SD 8.0), completed the study. The majority of participants were from minority populations (66% African American, 3% Latino, 3% Asian). Thirty three percent of participants reported an annual household income below $20,000. Eight percent were non-native English speakers. The majority of participants had low-level or no computer/Internet experience prior to the study.
 
 Methods – Collaborative learning strategies were used in small group hands-on computer classes to deliver a standardized curriculum (Helping Older Adults Search for Health Information Online: A Toolkit for Trainers from the National Institute on Aging). Strategies employed were: explicit statement of group/participatory nature of class, periodic peer shared reflection times during class, active encouragement of discussion between peers, hands-on work with partners, group discussion of real-life questions from participants, and structured shared reflection time at the close of each session. Participants were recruited through local advertisements. No incentive other than the free classes was offered. Groups met for two hours, twice a week for four weeks. Assessment was via pre and post-tests. General computing knowledge/skills were measured using objective tests of abilities. Questions from several established scales were adapted for additional assessment. E-health literacy was measured using questions of perceived skill and comfort in finding health information online; perceived usefulness of the Internet for help making health decisions; and perceived importance of the Internet for obtaining health information. Subjects were also asked to report on changes to their health behaviour/decision- making post intervention, and learning effort expended during the study. Additional questions measured psychological adjustment to later life, attitudes toward computers, attitude toward the aging experience, and attitude toward the collaborative learning method and the class. 
 
 Main Results – A dependent t-test analysis indicated strong significant gains post-test in computing/Internet knowledge and skills, and in e-health literacy efficacy (perceived skills/comfort with using the Internet for health information and decision-making). Pre-test results showed participants to be low on these measures, leaving much room for improvement. Perception of the usefulness and importance of the Internet for health decision-making also showed strong gains.
 
 Significant positive changes were also found in these areas of attitude: reduction of computer anxiety, attitude toward physical changes associated with aging, and improvement in attitude toward the collaborative learning method. 
 
 A majority of participants indicated altering health-related behaviours as a result of the class, including changing the way they think about diet or exercise, changing the way they cope with a condition, and changing their approach to maintaining health. 
 
 The results showed no significant change in self-esteem, self-efficacy, and psychological attitudes toward aging. Computer interest and efficacy also showed no significant change, perhaps because participants already measured high in these prior to the intervention.
 
 The amount of time participants spent preparing for class correlated significantly to e-health literacy efficacy and perceived importance of Internet health information, but not to other outcome measures. 
 
 Group composition (gender, peer familiarity, prior computer experience) did not affect outcomes; however composition was uneven, as groups were small and variable in size, and 71% of participants were female.
 
 Conclusion – The study supports the use of a collaborative learning approach to effectively deliver e-health literacy instruction to older adults in a community setting.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,173 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».