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Record W7017879943

Bridging the digital divide: examining the use and access to e-health based technologies by millennials and older adults in Ontario, Canada

2021· dissertation· en· W7017879943 on OpenAlexaboutno aff

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsDigital divideBridging (networking)The InternetDigital literacyInternet accessAccess to informationAdult literacyInformation and Communications Technology
DOInot available

Abstract

fetched live from OpenAlex

The digital divide is the gap between demographics and regions that have access to information communication technologies and those who do not. The older adult generation may not be familiar with e-Health usage, compared to generations such as millennials. A convenience non-random descriptive comparative study was conducted; Information was collected based on demographics; health information collection, usage and distribution; E-health and e-health technology usage; and digital literacy levels. Millennials (n=31) were undergraduate students recruited at Ontario Tech University; Older adults (n=28) were recruited from senior centres in the Durham region. Data was examined using sex and age cohort, to identify any statistically significant differences. Results showed that older adults had a decreased understanding of E-health based technologies, digital literacy, and accessed the internet less. These preliminary findings suggest that there are noted challenges facing older Canadians in terms of utilization of e-health technologies, in comparison to younger Canadians in Ontario.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.002
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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
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

Citations0
Published2021
Admission routes1
Has abstractyes

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