Relationships among technology use, social engagement, resilience, and access to healthcare
Bibliographic record
Abstract
The COVID-19 pandemic prompted measures to protect the most vulnerable populations that induced inequities by diminishing accessibility of healthcare services for older adults. It has been argued that assistive technologies can reduce health inequities by promoting access to healthcare through resilience and social engagement. We did a small exploratory study to investigate how technologies designed to support social engagement and resilience are related to healthcare-seeking behaviors and healthcare access with a sample of 8 community-dwelling older adults aged 65+. We hypothesized relationships among the following variables: technology use, social engagement, resilience, psychological impact of assistive devices, care-seeking behaviors, and access to healthcare. Variables were assessed using questionnaires administered in an interview format. The results give partial support to our hypotheses. For example, increased frequency and longer duration of technology use were correlated with improved social engagement. Increased social engagement and a positive psychosocial impact of assistive devices were associated with increased resilience. The findings demonstrate that technology can mitigating healthcare barriers by promoting social engagement, resilience, and care-seeking behaviors. Recommendations for future studies include using large sample sizes and a broader range of measures for the key constructs to produce generalizable findings consistent with our preliminary results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".