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Record W1488783403 · doi:10.18438/b8cp6b

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

2012· article· en· W1488783403 on OpenAlexaffvenue
Cari Merkley

Bibliographic record

VenueEvidence Based Library and Information Practice · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsMount Royal University
Fundersnot available
KeywordsThe InternetComputer literacyHealth literacyMedical educationIntervention (counseling)DemographicsScale (ratio)AnxietyMedicineGerontologyPsychologyFamily medicineHealth careNursingComputer scienceWorld Wide WebPsychiatry

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.361
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.327
Teacher spread0.275 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2012
Admission routes2
Has abstractyes

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